Devices, systems, and methods for virtual staining

ABSTRACT

The disclosure herein provides methods, systems, and devices for virtually staining biological tissue for enhanced visualization without use of an actual dye or tag by detecting how each pixel of an unstained tissue image changes in waveform after staining with a certain dye(s) and/or tag(s) or other transformation under a certain electromagnetic radiation source, developing a virtual staining transform based on such detection, and applying such virtual staining transform to an unstained biological tissue to virtually stain the tissue.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/911,098, entitled DEVICES, SYSTEMS, AND METHODS FOR VIRTUAL STAINING,filed Jun. 24, 2020 which is a continuation of U.S. patent applicationSer. No. 14/224,903, entitled DEVICES, SYSTEMS, AND METHODS FOR VIRTUALSTAINING, filed Mar. 25, 2014, which is a continuation of U.S. patentapplication Ser. No. 13/843,588, entitled DEVICES, SYSTEMS, AND METHODSFOR VIRTUAL STAINING, filed Mar. 15, 2013, which claims the benefit ofU.S. Provisional Patent Application Ser. No. 61/656,930, entitledDEVICES, SYSTEMS, AND METHODS FOR VIRTUAL STAINING, filed Jun. 7, 2012,and U.S. Provisional Patent Application Ser. No. 61/612,925, entitledDEVICES, SYSTEMS, AND METHODS FOR VIRTUAL STAINING, filed Mar. 19, 2012.Each of the foregoing applications is hereby incorporated by referenceherein in its entirety.

BACKGROUND Field

Embodiments relate to the field of imaging biological tissue, and, inparticular, to methods, systems, and devices for virtually stainingbiological tissue for enhanced visualization without actually stainingand/or tagging the tissue.

Description

With the development of new technologies, various stains and tags can beattached to biological tissues to enhance contrast of tissue componentsand thereby improve visibility. Different stains and tags can be used tocontrast tissues, cell populations, or organelles within individualcells and to visualize different tissue components depending on theneed. However, once a biological tissue is stained with a particularstain or tag to visualize one tissue component, the same tissuegenerally cannot be stained again with another dye or tag to visualizeanother tissue component. As such, visualizing another tissue componentby use of another dye or tag generally requires using a new tissuesample and increased costs.

SUMMARY

Advancements in technology make it possible to use hyperspectral imagingto virtually stain biological tissue for enhanced visualization withoutactually staining and/or tagging as described herein. Because the tissuesample is not actually stained, tagged, or otherwise altered, a singletissue sample can be virtually stained with various dyes, tags, and/orother transformations to allow enhanced visualization of as many tissuecomponents as desired. In addition, images of the same biological tissuevirtually stained with different dyes, tags, or other transformationscan be viewed side-by-side for a more comprehensive analysis. Also,virtual staining can be performed in vivo as well. In certainembodiments, it is also possible to use hyperspectral imaging to providedetailed and objective analysis of a biological tissue sample asdescribed herein.

In one embodiment, a computer-implemented method for virtually staininga tissue sample comprises directing by an electromagnetic radiationsource electromagnetic radiation within a bandwidth range on a tissuesample to be virtually stained, detecting by at least one detectiondevice electromagnetic radiation reflected or transmitted from thetissue sample, receiving electronically by a computing system thedetected electromagnetic radiation from the at least one detectiondevice, identifying by the computing system a waveform signatureassociated with each input pixel of an input image of the tissue sample,wherein the input image is generated based on the detectedelectromagnetic radiation, receiving by the computing systeminstructions to virtually stain the tissue sample with at least onevirtual stain, assigning by the computing system one or more outputpixels to each input pixel according to a virtual staining transform,wherein the virtual staining transform comprises mapping data for avirtual stain, wherein the mapping data is used to assign the outputpixel based on the waveform signature associated with the input pixel,and generating by the computing system an output image of the tissuesample based on the output pixels, wherein the computing systemcomprises a computer processor and an electronic storage medium. In someembodiments, the computing system in the above computer-implementedmethod for virtually staining a tissue sample can comprise one or morecomputer systems.

In the above computer-implemented method for virtually staining a tissuesample, the virtually stained image can be substantially identical to animage of the tissue sample when treated with an actual stain thatcorresponds to the virtual stain. In certain embodiments, the actualstain is a dye configured to color certain portions of the tissuesample. In other embodiments, the actual stain is a tag or probe. Inother embodiments, the tag or probe is at least one of a groupcomprising an antibody, an aptamer, and a fluorescent protein. The abovecomputer-implemented method for virtually staining a tissue sample canbe performed in vivo. The above computer-implemented method forvirtually staining a tissue sample can also be performed in vitro. Inthe above computer-implemented method for virtually staining a tissuesample, the at least one detection device can be at least one of a groupcomprising multi-spectrum detector, ultrasound detector, X-ray detector,MRI detector, CT, PET, and PET-CT. In the above computer-implementedmethod for virtually staining a tissue sample, the computing system canbe connected to the electromagnetic radiation source and the at leastone detection device over a computer network. Further, in the abovecomputer-implemented method for virtually staining a tissue sample, theelectromagnetic radiation source can direct at least one of a groupcomprising multi-spectrum electromagnetic radiation, X-ray spectrum,ultrasound spectrum, infrared spectrum, MRI spectrum, PET spectrum, andCT spectrum. In some embodiments, the above computer-implemented methodfor virtually staining a tissue sample further comprises using thedetermined waveform associated with each pixel to classify a particulardisease according to certain criteria. In other embodiments, the certaincriteria can comprise whether the particular disease is of a class ofdiseases that are susceptible to a certain treatment.

In one embodiment, a computer-implemented method of developing a virtualstaining transform comprises directing by an electromagnetic radiationsource electromagnetic radiation within a bandwidth range on a tissuesample, detecting by at least one detection device electromagneticradiation reflected or transmitted from the tissue sample, generating bya computing system a first image from the detected electromagneticradiation from the tissue sample, wherein the first image comprises aplurality of pixels, identifying by the computing system a waveformassociated with each one of the plurality of pixels forming the firstimage, modifying the tissue sample, directing by a light source visiblelight on the modified tissue sample, detecting by the at least onedetection device visible light reflected or transmitted from themodified tissue sample, generating by the computing system a secondimage from the detected visible light from the modified tissue sample,wherein the second image comprises a plurality of pixels, identifying bythe computing system a color composition of each one of the plurality ofpixels forming the second image, generating by the computing system avirtual staining transform based on the identified waveform associatedwith each one of the plurality of pixels forming the first image and theidentified color composition of each one of the plurality of pixelsforming the second image, and storing in the computing system thevirtual staining transform, wherein the computing system comprises acomputer processor and an electronic storage medium.

The computer-implemented method of developing a virtual stainingtransform can further comprise repeating the method for a plurality oftissue samples and combining by the computing system the colorcomposition identified from the second image that corresponds toidentical or substantially identical first waveforms according to apre-stored algorithm. In the above computer-implemented method ofdeveloping a virtual staining transform, the modifying can comprisestaining the tissue sample with a stain in some embodiments. In otherembodiments, the modifying can comprise attaching at least one tag tothe tissue sample. In certain embodiments, the tag can be at least oneof a group comprising an antibody, an aptamer, and a fluorescentprotein. In the above computer-implemented method of developing avirtual staining transform, the at least one detection device can be atleast one of a group comprising multi-spectrum detector, ultrasounddetector, X-ray detector, Mill detector, CT, PET, and PET-CT. Also inthe above computer-implemented method of developing a virtual stainingtransform, the electromagnetic radiation source can direct at least oneof a group comprising multi-spectrum electromagnetic radiation, X-rayspectrum, ultrasound spectrum, infrared spectrum, MM spectrum, PETspectrum, and CT spectrum. In the above computer-implemented method ofdeveloping a virtual staining transform, the computing system cancomprise one or more computer systems. Further, in the abovecomputer-implemented method of developing a virtual staining transform,the computing system can be connected to the electromagnetic radiationsource and the at least one detection device over a computer network.

In one embodiment, a computer-readable, non-transitory storage mediumhas a computer program stored thereon for causing a suitably programmedcomputer system to process by one or more computer processorscomputer-program code by performing a method when the computer programis executed on the suitably programmed computer system, wherein themethod comprises directing by an electromagnetic radiation sourceelectromagnetic radiation within a bandwidth range on a tissue sample tobe virtually stained, detecting by at least one detection deviceelectromagnetic radiation reflected or transmitted from the tissuesample, receiving electronically by a computing system the detectedelectromagnetic radiation from the at least one detection device,identifying by the computing system a waveform signature associated witheach input pixel of an input image of the tissue sample, wherein theinput image is generated based on the detected electromagneticradiation, receiving by the computing system instructions to virtuallystain the tissue sample with at least one virtual stain, assigning bythe computing system one or more output pixels to each input pixelaccording to a virtual staining transform, wherein the virtual stainingtransform comprises mapping data for a virtual stain, wherein themapping data is used to assign the output pixel based on the waveformsignature associated with the input pixel, and generating by thecomputing system an output image of the tissue sample based on theoutput pixels, wherein the computing system comprises a computerprocessor and an electronic storage medium.

In one embodiment, a computer-readable, non-transitory storage mediumhas a computer program stored thereon for causing a suitably programmedcomputer system to process by one or more computer processorscomputer-program code by performing a method when the computer programis executed on the suitably programmed computer system, wherein themethod comprises directing by an electromagnetic radiation sourceelectromagnetic radiation within a bandwidth range on a tissue sample,detecting by at least one detection device electromagnetic radiationreflected or transmitted from the tissue sample, generating by acomputing system a first image from the detected electromagneticradiation from the tissue sample, wherein the first image comprises aplurality of pixels, identifying by the computing system a waveformassociated with each one of the plurality of pixels forming the firstimage, modifying the tissue sample, directing by a light source visiblelight on the modified tissue sample, detecting by the at least onedetection device visible light reflected or transmitted from themodified tissue sample, generating by the computing system a secondimage from the detected visible light from the modified tissue sample,wherein the second image comprises a plurality of pixels, identifying bythe computing system a color composition of each one of the plurality ofpixels forming the second image, generating by the computing system avirtual staining transform based on the identified waveform associatedwith each one of the plurality of pixels forming the first image and theidentified color composition of each one of the plurality of pixelsforming the second image, and storing in the computing system thevirtual staining transform, wherein the computing system comprises acomputer processor and an electronic storage medium.

In one embodiment, a system for virtually staining a tissue samplecomprises an electromagnetic radiation source configured to directelectromagnetic radiation within a bandwidth on a tissue sample to bevirtually stained, at least one detection device configured to detectelectromagnetic radiation reflected or transmitted from the tissuesample, and a storage computer system comprising a computer processorconfigured to execute modules comprising at least, a data receivingmodule configured to receive electronically the detected electromagneticradiation from the at least one detection device, a pixel analysismodule configured to identify a waveform signature associated with eachinput pixel of an input image of the tissue sample, wherein the inputimage is generated based on the detected electromagnetic radiation, auser instructions module configured to receive instructions to virtuallystain the tissue sample with at least one virtual stain, a virtualtransform module configured to assign one or more output pixels to eachinput pixel according to a virtual staining transform, wherein thevirtual staining transform comprises mapping data for a virtual stain,wherein the mapping data is used to assign the output pixel based on thewaveform signature associated with the input pixel, and an imagegeneration module configured to generate an output image of the tissuesample based on the output pixels.

In one embodiment, a system for developing a virtual staining transformcomprises an electromagnetic radiation source configured to directelectromagnetic radiation within a bandwidth range on a tissue sample,at least one detection device configured to detect electromagneticradiation reflected or transmitted from the tissue sample, a lightsource configured to direct visible light on a modified tissue sample,at least one detection device configured to detect visible reflected ortransmitted from the modified tissue sample, and a storage computersystem comprising a computer processor configured to execute modulescomprising at least an initial image generation module configured togenerate a first image from the detected electromagnetic radiation fromthe tissue sample, wherein the first image comprises a plurality ofpixels, an initial pixel analysis module configured to identify awaveform associated with each one of the plurality of pixels forming thefirst image, a final image generation module configured to generate asecond image from the detected visible from the modified tissue sample,wherein the second image comprises a plurality of pixels, a final pixelanalysis module configured to identify a color composition of each oneof the plurality of pixels forming the second image, a virtual stainingtransform generation module configured to generate a virtual stainingtransform based on the identified waveform associated with each one ofthe plurality of pixels forming the first image and the identified colorcomposition of each one of the plurality of pixels forming the secondimage, and a virtual staining transform storage module configured tostore the virtual staining transform.

In one embodiment, a computer-implemented method for virtually staininga tissue sample comprises obtaining by a computing system an electronicimage of the tissue sample, determining by the computing system a vectorsignature or waveform signature associated with each pixel in theelectronic image, generating by the computing system an output pixel foreach pixel in the electronic image based on inputting the determinedvector signature or waveform signature into a virtual stainingtransform, and outputting by the computing system a virtually stainedimage of the tissue sample based on the generated output pixels, whereinthe computing system comprises a computer processor and an electronicstorage medium. In some embodiments, the computing system in the abovecomputer-implemented method for virtually staining a tissue sample cancomprise one or more computer systems.

In the above computer-implemented method for virtually staining a tissuesample, the virtually stained image can be substantially identical to animage of the tissue sample when stained with an actual stain. In someembodiments, the actual stain can be a dye configured to color certainportions of the tissue sample. In other embodiments, the actual staincan be a tag or probe. In certain embodiments, the tag or probe can beat least one of a group comprising an antibody, an aptamer, and afluorescent protein. The above computer-implemented method for virtuallystaining a tissue sample can be performed in vivo. The abovecomputer-implemented method for virtually staining a tissue sample canalso be performed in vitro.

In one embodiment, a computer-readable, non-transitory storage mediumhas a computer program stored thereon for causing a suitably programmedcomputer system to process by one or more computer processorscomputer-program code by performing a method when the computer programis executed on the suitably programmed computer system, wherein themethod comprises obtaining by a computing system an electronic image ofthe tissue sample, determining by the computing system a vectorsignature or waveform signature associated with each pixel in theelectronic image, generating by the computing system an output pixel foreach pixel in the electronic image based on inputting the determinedvector signature or waveform signature into a virtual stainingtransform, and outputting by the computing system a virtually stainedimage of the tissue sample based on the generated output pixels, whereinthe computing system comprises a computer processor and an electronicstorage medium.

In one embodiment, a system for virtually staining a tissue samplecomprises a storage computer system comprising a computer processorconfigured to execute modules comprising at least a data receivingmodule configured to obtain electronically an electronic image of thetissue sample, a pixel analysis module configured to determine a vectorsignature or waveform signature associated with each pixel in theelectronic image, a virtual transform module configured to generate anoutput pixel for each pixel in the electronic image based on inputtingthe determined vector signature or waveform signature into a virtualstaining transform, and an output module configured to output avirtually stained image of the tissue sample based on the generatedoutput pixels.

For purposes of this summary, certain aspects, advantages, and novelfeatures are described herein. It is to be understood that notnecessarily all such advantages may be achieved in accordance with anyparticular embodiment. Thus, for example, those skilled in the art willrecognize that the invention may be embodied or carried out in a mannerthat achieves one advantage or group of advantages as taught hereinwithout necessarily achieving other advantages as may be taught orsuggested herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features, aspects and advantages are describedin detail below with reference to the drawings of various embodiments,which are intended to illustrate and not to limit the disclosure. Thedrawings comprise the following figures in which:

FIG. 1A is a block diagram depicting a high level overview of oneembodiment of a standalone system or software system for virtuallystaining biological tissue.

FIG. 1B is a block diagram depicting a high level overview of oneembodiment of a system for virtually staining biological tissue remotelyby providing virtual staining services over a network.

FIG. 2 is a block diagram depicting one embodiment of a computerhardware system configured to run software for implementing one or moreembodiments of the virtual staining system described herein.

FIGS. 3A-3B are block diagrams depicting overviews of embodiments ofmethods of collecting data and building a virtual staining transform fora particular stain under different types of electromagnetic radiation.

FIGS. 4A-4F are block diagrams depicting overviews of embodiments ofmethods of collecting data and building virtual staining transforms.

FIGS. 5A-5E are block diagrams depicting overviews of embodiments ofmethods of virtually staining a sample imaged by various imagingtechniques or sources.

FIGS. 6A-6B are block diagrams depicting overviews of embodiments ofmethods of collecting data and building a virtual staining transform fora particular stain under multiple electromagnetic radiation sources.

FIGS. 7A-7B are block diagrams depicting overviews of embodiments ofmethods of collecting data and building virtual staining transforms formultiple stains under multiple electromagnetic radiation sources.

FIGS. 8A-8B are block diagrams depicting overviews of embodiments ofmethods of collecting data and building a three-dimensional virtualstaining transform for a particular stain.

FIGS. 9A-9B are block diagrams depicting overviews of embodiments ofmethods of collecting data and building a virtual staining transform fora particular stain with volume averaging.

FIG. 10 is a block diagram depicting an overview of one embodiment of amethod of virtually staining a tissue sample with different virtualstains and under different electromagnetic radiation sources using avirtual staining transform.

FIG. 11 is a block diagram depicting an overview of some embodiments ofmethods of virtually staining that generate more than one output pixelfor each input pixel.

FIGS. 12A-12B are block diagrams depicting overviews of embodiments ofmethods of virtually staining a single tissue sample with multiplevirtual stains using a virtual staining transform.

FIG. 12C depicts an example of one embodiment of a screen view of asingle tissue sample virtually stained by multiple virtual stains.

FIGS. 13A-13D depict examples of virtually stained images in comparisonto an actually stained slide and spectral data of underlying pixels.

FIG. 14 depicts an example of a virtually stained image.

FIGS. 15A and 15B depict examples of identification of biologicalspecies using hyperspectral imaging.

FIG. 16 depicts an example of one embodiment of a method of usinghyperspectral imaging and detected waveforms to further classify atissue(s) with colon cancer.

FIG. 17 depicts an example of one embodiment of a method of usinghyperspectral imaging and detected waveforms to further classifymoderately differentiated colonic adenocarcinoma according tohistopathologic classifications.

FIG. 18 depicts an example of one embodiment of a method of usinghyperspectral imaging and detected waveforms to profile specimens acrossa microenvironment.

FIG. 19 depicts an example of one embodiment of a method of usinghyperspectral imaging and detected waveforms to further classify andcompare different tumor types.

FIG. 20 depicts an example of one embodiment of a method of usinghyperspectral imaging and detected waveforms to quantitatively grade apathological disease or condition.

FIG. 21 is a block diagram depicting an overview of one embodiment ofusing vector signature analysis to analyze each pixel of a detectedimage of a tissue sample.

FIG. 22 depicts an example medical probe into which a virtual stainingdevice may be incorporated.

FIG. 23 depicts another example medical probe into which a virtualstaining device may be incorporated.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Embodiments will now be described with reference to the accompanyingfigures. The terminology used in the description presented herein is notintended to be interpreted in any limited or restrictive manner, simplybecause it is being utilized in conjunction with a detailed descriptionof certain specific embodiments. Furthermore, embodiments may compriseseveral novel features, no single one of which is solely responsible forits desirable attributed or which is essential to practicing theembodiments herein described.

As used herein, the terms “sample,” “tissue sample,” “biologicalsample,” and “specimen” may be used interchangeably, and the foregoingterms comprise without limitation tissue samples, tissue specimen, bulktissue, surgical site, site, bacteria, cell, cell components, asubstance on an agar plate, or any material or surface off of whichelectromagnetic radiation can be reflected. A sample can be analyzed invivo or in vitro.

As used herein, the terms “camera,” “camera device,” “detector,”“detector device,” “receiver,” and “receiver device” may be usedinterchangeably, and the foregoing terms comprise without limitation amulti-spectrum detector, ultrasound detector, X-ray detector, MRIdetector, CT, PET, PET-CT, or any device capable of detecting reflectedor transmitted radiation of some sort.

As used herein, the terms “stain” and “staining” are broad terms and caninclude without limitation staining with a dye or a stain,immunohistochemical staining, aptamer staining, tagging, chemicalstaining, antibody staining, or any other alteration to a tissue sample.

As used herein, the terms “pixel,” “group of pixels,” “unit of pixels”or the like are broad terms and can include without limitation anyindividual unit of pixel or pixels of an image. The term “pixel” as usedherein is a broad term and can include without limitation a point orarea in an image. The term “pixel waveform” as used herein is a broadterm and can include without limitation a waveform detected at aposition of a particular pixel. The waveform is a representation of thedetected spectrum wavelength(s) and amplitude(s) at a particular pointin the image. They are not to be limited to refer to any particular unitof pixel or pixels.

The disclosure herein provides methods, systems, and devices forvirtually staining biological tissue for enhanced visualization withoutuse of an actual dye or tag. In an embodiment, virtual staining isaccomplished by detecting waveforms associated with the position of eachpixel of an unstained tissue sample and applying a virtual stainingtransform to each of those pixels to generate an output image that issubstantially similar to an image of the tissue sample when stained withan actual stain or other desired transform. A virtual staining transformcan be developed by detecting waveforms associated with the position ofeach pixel of an unstained tissue sample and the result of the samepixel after staining, tagging, or other transformation or alterationwhen viewed under a particular type of light, such as visible light forexample.

With the development of new technologies, biological tissues can bestained with various dyes and/or attached with various tags to enhancecontrast of tissue components and thereby improve visibility. Differentstains and/or tags can be used to contrast bulk tissues, cellpopulations, and/or organelles within individual cells, and/or tovisualize different tissue components depending on the need. However,once a biological tissue is stained with a particular dye and/or tag tovisualize one tissue component, the same tissue generally cannot bestained again with another dye and/or tag to visualize another tissuecomponent. As such, visualizing another tissue component by use ofanother dye or tag generally requires using a new tissue sample, whichmay or may not exhibit the same characteristics, or which may or may notbe available.

By employing the methods, systems, and devices for virtually stainingbiological tissue for enhanced visualization described herein, one cangenerate virtually stained images of a biological tissue withoutactually staining or tagging the tissue. Because the tissue sample isnot actually stained with a dye and/or a tag, the same tissue sample canbe virtually stained with various dyes and tags to allow enhancedvisualization of as many tissue components as desired. In addition,images of the same biological tissue virtually stained with differentdyes and tags can be viewed side-by-side for a more comprehensiveanalysis and/or for a direct one to one comparison of the tissue sample.Furthermore, virtual staining can be performed in vivo as well, allowingexaminers to observe virtually stained tissue images without having tosurgically extract or isolate the sample to be examined from thesurrounding tissue.

In some embodiments, electromagnetic radiation is directed at a tissuesample. A detection device detects electromagnetic radiation that istransmitted, reflected, or otherwise not absorbed by the tissue sample.In an embodiment, none of the spectrum data is subtracted or otherwisediscarded but rather the system is configured to analyze the entirespectrum data available at each point of an image. The whole spectrumdata is used by an image generating device or system to generate aninitial image and a computing system analyzes the contents of thegenerated image pixel-by-pixel or according some predetermined unit ofpixels. The chemical properties of each pixel or group of pixels isdisclosed in the reflected, transmitted, or otherwise not absorbed lightby the tissue sample in the form of a waveform or waveform signature.Accordingly, using a pre-developed and pre-stored virtual stainingtransform, the computing system can apply the transform to each pixel toobtain an output of each pixel or group of pixels after virtuallystaining with a virtual stain, tag, or transform of choice.

The virtual staining transform comprises data that can map an inputpixel of a certain waveform to an output pixel and/or an associatedoutput waveform. A single input pixel of a certain waveform can beassociated with more than one output pixel, wherein each output pixelcorresponds to the result of an input pixel after virtually stainingwith a particular stain, dye, or the like. The output pixel can be of aparticular color or grayscale. Such output pixels are combined by thecomputing system to generate a virtually stained image of the tissuesample. In other words, the computing system pseudo-colors the initialdetected image to produce a virtually stained image.

In an embodiment, the system disclosed herein is distinguishable fromother known methods and applications of imaging and hyperspectralimaging, such as with quantum dots. For example, in some embodiments,the system can be configured to analyze what would be consideredbackground spectra for other imaging applications. Other imagingapplications in the life sciences, such as in connection with quantumdots, are generally employed to search for specific components orirregularities in the sample that are often tagged or labeled withreporter molecules. Because the purpose is to specifically locate andimage those tagged and/or probed components, background spectra from thenon-labeled portions of the sample are simply subtracted for variousreasons, such as for faster processing. However, in certain embodimentsof the system illustrated herein, the system can be configured toanalyze only the background spectra, which would have been subtracted byother hyperspectral imaging applications, as opposed to analyzing theentire spectra. Alternatively, in other embodiments, the system can beconfigured to analyze the entire spectra detected from the tissuesample. Because the systems illustrated here focus on the backgroundspectra, the entire spectrum, or portions thereof, the system can beconfigured to better visualize different tissue or cellularcharacteristics and/or overlapping features or entities, which can beobserved at the same time. Such vast data is subsequently analyzed todetermine the waveform associated with each pixel, which is in turnmapped according to the virtual staining transform.

FIG. 1A is a block diagram illustrating a high level overview of oneembodiment of a standalone system or software system for virtuallystaining biological tissue. In the depicted embodiment, a main computingsystem 110 is connected to at least one of a light source orelectromagnetic radiation source or laser 102, a camera device ordetector device or receiver 104, a user interface 106, and a display foroutputting virtually stained images 126.

A user instructs the main computing system 110 via the user interface106 to direct an electromagnetic radiation (EMR) source 102 at a sample.The detector device 104 detects reflected, transmitted, or otherwise notabsorbed radiation from the sample and sends the detected data to themain computing system 110. A user instructs the main computing system110 via the user interface 106 to apply a particular virtual stain, tag,or other transformation to the detected image. The main computing system110 can be configured to generate a virtually stained image according tothe user input, and the virtually stained image can be displayed on adisplay for outputting virtually stained images 126.

In certain embodiments, the main computing system 110, as depicted inFIG. 1A, comprises but is not limited to a billing module 112, an EMRsource selection module 114, a web server 116, a virtual staining module118, an initial image processing module 120, a final image generationmodule 122, a virtual staining transformation database 124, and a usercontrolled adjustment virtual stain module 128. In some embodiments, themain computing system 110 can be configured, for example, among otherthings, to: communicate with the user interface 106; instruct the EMRsource 102 to direct a certain electromagnetic radiation at a sample;receive detected image data from the detector device 104; virtuallystain or otherwise transform the detected image; instruct a display foroutputting virtually stained images 126 to display the virtually stainedor otherwise transformed image; and/or enable billing processes for eachtransformation of images.

Upon receiving input from a user interface 106, the main computingsystem 110 instructs the EMR source 102 to direct a particular EMRsource to the sample. In some embodiments, the EMR source selectionmodule 114 of the main computing system 110 is configured to instructthe EMR source 102 to direct a particular EMR source to the sample. Incertain embodiments, the EMR source can be configured to directmulti-spectrum electromagnetic radiation, X-ray, ultrasound, infrared,other electromagnetic radiation, MM, CT, PET, PET-CT, or any combinationthereof. After the detector device 104 detects the transmitted orreflected radiation from the sample, such detected image data isanalyzed by the main computing system 110 or the initial imageprocessing module 120 thereof in some embodiments.

In some embodiments, the received image data is analyzed pixel by pixel,point by point, or area by area. In other embodiments, the receivedimage data is analyzed according to a preset group of pixels. In someembodiments, the image data comprises waveform data associated with eachpixel position in the image. For example, for each pixel in an image,there is an associated waveform signature. The waveform signature canrepresent the detected wavelengths and corresponding amplitudes that aredetected by the detector at each position in the tissue sample (forexample, see waveform signature 416 in FIG. 4A). Each waveformcorresponding to each pixel or group of pixels is analyzed by the maincomputing system 110 or initial image processing module 120.

In certain embodiments, after the initial image is analyzed orconcurrently, the initial image is virtually stained or otherwisetransformed by the main computing system 110 or a virtual stainingmodule 118 thereof. To do so, in some embodiments, the virtual stainingmodule 118 accesses a virtual staining transformation database 124. Thevirtual staining transformation database 124 contains data related tohow a pixel or group of pixels associated with a particular waveform istransformed when actually stained, when a tag is attached, or some othertransform or alteration. In some embodiments, such data can be updatedperiodically or in real-time.

In some embodiments, once the virtual staining module 118 virtuallystains or otherwise transforms each pixel or group of pixels of theinitial image or concurrently, the final image generation module 122combines each transformed pixel or group of pixels to generate a finaltransformed image. This virtually stained or otherwise transformed imageis displayed to the user on a display for outputting virtually stainedimages 126.

A user can, in some embodiments, instruct the virtual staining module118 to apply a particular stain or transformation to the initial imagevia the user interface 106. The user interface communicates the user'sinstructions to the user controlled adjustment virtual stain module 128.In some embodiments, the user controlled adjustment virtual stain module128 is further configured to receive user instructions from the userinterface 106 before or after a virtually stained image is generated tomake slight changes in the virtual staining. For example, the user caninstruct the user controlled adjustment virtual stain module 128 toapply different virtual stains to different portions of the initialimage, to apply less or more of a certain virtual stain to a particularportion of the initial image, or to enhance resolution of a particularportion of the virtually stained image.

In some embodiments, the standalone device comprises the main computingsystem, some or all of its components, and a user interface, which areconnected to a conventional light source, EMR source, or laser a cameradevice or a detector, and a display. In other embodiments, thestandalone device comprises all of the above components or some subsetthereof. In yet other embodiments, a software system is configured toinstruct and use conventional system components to obtain the functionsdescribed above. In certain embodiments, the system components discussedabove or a subset thereof are conventional devices that are widelyavailable.

FIG. 1B is a block diagram illustrating a high level overview of oneembodiment of a system for virtually staining biological tissue byremotely providing services over a network. For example, the maincomputing system 110 can be configured to receive or access over anelectronic network images of a tissue sample to be processed andvirtually stained by the computing system 110. In this embodiment, theimages are generated at a location remote or distinct from the computingsystem. The images can be stored in a database that is remote from thecomputing system 110 or the images can be transmitted to the computingsystem 110 through an electronic network. The computing system 110 canbe configured to transmit the virtually stained image to a remotelocation or store the virtually stained image in a database located in aremote location. Alternatively, as in the depicted embodiment, a maincomputing system 110 is connected, directly or indirectly, to at leastone detection device 104 and a user interface 106 over a computernetwork 108. In some embodiments, at least one electromagnetic radiationsource 102 is also connected to the main computing system 110 via thecomputer network 108. In other embodiments, the at least oneelectromagnetic radiation source 102 is not connected to the maincomputing system 110 over the computer network 108 and is locallymaintained and controlled. In yet other embodiments, the at least oneelectromagnetic radiation source 102 is connected to the at least onedetection device 104.

The network may comprise one or more internet connections, securepeer-to-peer connections, secure socket layer (SSL) connections over theinternet, virtual private network (VPN) connections over the internet,or other secure connections over the internet, private networkconnections, dedicated network connections (for example, IDSN, T1, orthe like), wireless or cellular connections, or the like or anycombination of the foregoing.

In some embodiments, a user can select using the user interface 106 aparticular electromagnetic radiation source 102 to be directed at atissue sample. In other embodiments, the particular electromagneticradiation source 102 to be directed at a tissue sample is selectedlocally via another user interface that is not in communication with themain computing system 110.

The selected at least one electronic radiation source is directed at thetissue sample. The at least one detection device 104 detectselectromagnetic radiation that is transmitted, reflected, or otherwisenot absorbed by the tissue sample. Such detected data is subsequentlytransmitted to the main computing system 110 over the computer network.

In some embodiments, an initial image processing module 120 of the maincomputing system 110 receives the data from the at least one detectiondevice 104 and analyzes the received data in a similar manner asdescribed above in relation to FIG. 1A. In certain embodiments, once theinitial image is analyzed by the initial image processing module 120 orconcurrently, the initial image is virtually stained or otherwisetransformed by a virtual staining module 118 in a similar manner asdescribed above in relation to FIG. 1A.

In some embodiments, once the virtual staining module 118 virtuallystains or otherwise transforms each pixel or group of pixels of theinitial image or concurrently, the final image generation module 122combines each transformed pixel or group of pixels to generate a finaltransformed image. This virtually stained or otherwise transformed imageis transmitted to the user interface 106 over the computer network 108.In other embodiments, the virtually stained or otherwise transformedimage is transmitted to another computing system or a mobile device ofthe user's choice via the web server 116 and the computer network 108.The user interface, another computing system, or mobile device candisplay the virtually stained or otherwise transformed images to theuser. In certain embodiments, a billing module 112 of the main computingsystem 110 generates a bill depending on the number of different virtualstains applied and/or the number of different samples that werevirtually stained or otherwise transformed.

A user can, in an embodiment, instruct the virtual staining module 118to apply a particular stain or transformation to the initial image viathe user interface 106 and over the computer network. The user interfacecommunicates the user's such instructions to the user controlledadjustment virtual stain module 128. In some embodiments, the usercontrolled adjustment virtual stain module 128 is further configured toreceive user instructions from the user interface 106 before or after avirtually stained image is generated to make slight changes in thevirtual staining as described above in relation to FIG. 1A.

In some embodiments, as illustrated in FIG. 1B, the main computingsystem 110 is not physically connected to the electromagnetic radiationsource 102 or the detection device 104. Rather they are connected over anetwork 108. In such embodiments, a user need not purchase or locallystore contents of the virtual staining transformation database 124, butcommunicates with the main computing system 110 located at a third-partylocation for such purposes.

Computing System

In some embodiments, the computer clients and/or servers described abovetake the form of a computing system 200 illustrated in FIG. 2 , which isa block diagram of one embodiment of a computing system that is incommunication with one or more computing systems 110 and/or one or moredata sources 115 via one or more networks 210. The computing system 200may be used to implement one or more of the systems and methodsdescribed herein. In addition, in one embodiment, the computing system200 may be configured to virtually stain or otherwise transform asample. While FIG. 2 illustrates one embodiment of a computing system200, it is recognized that the functionality provided for in thecomponents and modules of computing system 200 may be combined intofewer components and modules or further separated into additionalcomponents and modules.

Virtual Staining Module

In one embodiment, the system 200 comprises a virtual staining module206 that carries out the functions described herein with reference totransforming an initial image received from a detection device 104configured to detect reflected and transmitted electromagnetic radiationoff of a sample. The virtual staining module 206 may be executed on thecomputing system 200 by a central processing unit 204 discussed furtherbelow.

In general, the word “module,” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,possibly having entry and exit points, written in a programminglanguage, such as, for example, COBOL, CICS, Java, Lua, C or C++. Asoftware module may be compiled and linked into an executable program,installed in a dynamic link library, or may be written in an interpretedprogramming language such as, for example, BASIC, Perl, or Python. Itwill be appreciated that software modules may be callable from othermodules or from themselves, and/or may be invoked in response todetected events or interrupts. Software instructions may be embedded infirmware, such as an EPROM. It will be further appreciated that hardwaremodules may be comprised of connected logic units, such as gates andflip-flops, and/or may be comprised of programmable units, such asprogrammable gate arrays or processors. The modules described herein arepreferably implemented as software modules, but may be represented inhardware or firmware. Generally, the modules described herein refer tological modules that may be combined with other modules or divided intosub-modules despite their physical organization or storage.

Computing System Components

In one embodiment, the computing system 200 also comprises a mainframecomputer suitable for controlling and/or communicating with largedatabases, performing high volume transaction processing, and generatingreports from large databases. The computing system 200 also comprises acentral processing unit (“CPU”) 204, which may comprise a conventionalmicroprocessor. The computing system 200 further comprises a memory 205,such as random access memory (“RAM”) for temporary storage ofinformation and/or a read only memory (“ROM”) for permanent storage ofinformation, and a mass storage device 201, such as a hard drive,diskette, or optical media storage device. Typically, the modules of thecomputing system 200 are connected to the computer using a standardsbased bus system. In different embodiments, the standards based bussystem could be Peripheral Component Interconnect (PCI), Microchannel,SCSI, Industrial Standard Architecture (ISA) and Extended ISA (EISA)architectures, for example.

The computing system 200 comprises one or more commonly availableinput/output (I/O) devices and interfaces 203, such as a keyboard,mouse, touchpad, and printer. In one embodiment, the I/O devices andinterfaces 203 comprise one or more display devices, such as a monitor,that allows the visual presentation of data to a user. Moreparticularly, a display device provides for the presentation of GUIs,application software data, and multimedia presentations, for example. Inthe embodiment of FIG. 2 , the I/O devices and interfaces 203 alsoprovide a communications interface to various external devices. Thecomputing system 200 may also comprise one or more multimedia devices202, such as speakers, video cards, graphics accelerators, andmicrophones, for example.

Computing System Device/Operating System

The computing system 200 may run on a variety of computing devices, suchas, for example, a server, a Windows server, an Structure Query Languageserver, a Unix server, a personal computer, a mainframe computer, alaptop computer, a cell phone, a personal digital assistant, a kiosk, anaudio player, and so forth. The computing system 200 is generallycontrolled and coordinated by operating system software, such as z/OS,Windows 95, Windows 98, Windows NT, Windows 2000, Windows XP, WindowsVista, Windows 7, Linux, BSD, SunOS, Solaris, or other compatibleoperating systems. In Macintosh systems, the operating system may be anyavailable operating system, such as MAC OS X. In other embodiments, thecomputing system 200 may be controlled by a proprietary operatingsystem. Conventional operating systems control and schedule computerprocesses for execution, perform memory management, provide file system,networking, and I/O services, and provide a user interface, such as agraphical user interface (“GUI”), among other things.

Network

In the embodiment of FIG. 2 , the computing system 200 is coupled to anetwork 108, such as a LAN, WAN, or the Internet, for example, via awired, wireless, or combination of wired and wireless, communicationlink 215. The network 108 communicates with various computing devicesand/or other electronic devices via wired or wireless communicationlinks. In the embodiment of FIG. 2 , the network 108 is communicatingwith one or more computing systems 110 and/or one or more data sources115.

Access to the virtual staining module 206 of the computer system 200 bycomputing systems 110 and/or by data sources 115 may be through aweb-enabled user access point such as the computing systems' 110 or datasource's 115 personal computer, cellular phone, laptop, or other devicecapable of connecting to the network 108. Such a device may have abrowser module is implemented as a module that uses text, graphics,audio, video, and other media to present data and to allow interactionwith data via the network 108.

The browser module may be implemented as a combination of an all pointsaddressable display such as a cathode-ray tube (CRT), a liquid crystaldisplay (LCD), a plasma display, or other types and/or combinations ofdisplays. In addition, the browser module may be implemented tocommunicate with input devices 203 and may also comprise software withthe appropriate interfaces which allow a user to access data through theuse of stylized screen elements such as, for example, menus, windows,dialog boxes, toolbars, and controls (for example, radio buttons, checkboxes, sliding scales, and so forth). Furthermore, the browser modulemay communicate with a set of input and output devices to receivesignals from the user.

The input device(s) may comprise a keyboard, roller ball, pen andstylus, mouse, trackball, voice recognition system, or pre-designatedswitches or buttons. The output device(s) may comprise a speaker, adisplay screen, a printer, or a voice synthesizer. In addition a touchscreen may act as a hybrid input/output device. In another embodiment, auser may interact with the system more directly such as through a systemterminal connected to the score generator without communications overthe Internet, a WAN, or LAN, or similar network.

In some embodiments, the system 200 may comprise a physical or logicalconnection established between a remote microprocessor and a mainframehost computer for the express purpose of uploading, downloading, orviewing interactive data and databases on-line in real time. The remotemicroprocessor may be operated by an entity operating the computersystem 200, including the client server systems or the main serversystem, an/or may be operated by one or more of the data sources 115and/or one or more of the computing systems. In some embodiments,terminal emulation software may be used on the microprocessor forparticipating in the micro-mainframe link.

In some embodiments, computing systems 110 who are internal to an entityoperating the computer system 200 may access the virtual staining module206 internally as an application or process run by the CPU 204.

User Access Point

In an embodiment, a user access point or user interface 106 comprises apersonal computer, a laptop computer, a cellular phone, a GPS system, aBlackberry® device, a portable computing device, a server, a computerworkstation, a local area network of individual computers, aninteractive kiosk, a personal digital assistant, an interactive wirelesscommunications device, a handheld computer, an embedded computingdevice, or the like.

Other Systems

In addition to the systems that are illustrated in FIG. 2 , the network108 may communicate with other data sources or other computing devices.The computing system 200 may also comprise one or more internal and/orexternal data sources. In some embodiments, one or more of the datarepositories and the data sources may be implemented using a relationaldatabase, such as DB2, Sybase, Oracle, CodeBase and Microsoft® SQLServer as well as other types of databases such as, for example, a flatfile database, an entity-relationship database, and object-orienteddatabase, and/or a record-based database.

Overview of Developing a Virtual Staining Transform

In some embodiments, as illustrated in FIGS. 3A-3B, data of varioustissue samples that are actually stained with various stains iscollected and stored in order to build a virtual staining transformationdatabase. FIGS. 3A-3B are block diagrams depicting overviews ofembodiments of computer-implemented methods of collecting data andbuilding a virtual staining transform for a particular stain, tag, orother transform. The computer-implemented method can be employed for oneor more different types of electromagnetic radiation. In someembodiments, the whole process or selected blocks of FIGS. 3A-3B arerepeated for different stains.

Specifically, at block 302, a particular electromagnetic radiationsource is selected, which is subsequently directed at an unstainedtissue sample or other sample at block 304. The particularelectromagnetic radiation can be multi-spectrum electromagneticradiation, visible light, X-ray, ultrasound, infrared, Mill, PET and/orCT, or any other imaging modality currently existing or to be developedin the future. For example, the electromagnetic radiation can be ofwavelengths from about 400 nm to about 900 nm or above or other lightwith a wavelength band of any other range. In some embodiments,electromagnetic radiation that is transmitted, reflected, or otherwisenot absorbed by the sample is detected by at least one detection deviceat block 306. In certain embodiments, more than one detection device canbe utilized to reduce error in detection or to collect three-dimensionaldata.

Based on the detected data, an initial image is generated at block 308.In some embodiments, each pixel or group of pixels in the initial imageis associated with a waveform signature detected based on the detecteddata. In some embodiments, a computing system analyzes this initialimage and detects the waveform associated with each pixel or group ofpixels at block 310. In some embodiments, the computing system isconfigured to determine the chemical properties of tissue at aparticular pixel point or group of pixels. In certain embodiments,blocks 302 through 310 can be repeated to store initial images and theassociated waveform signature data of unstained tissue samples underdifferent types of electromagnetic radiation sources as depicted by 312.

In some embodiments, the sample is actually stained with a dye(s) or isattached to a tag(s) at block 314. Further, in the embodimentillustrated in FIG. 3A, an electromagnetic radiation source is selectedat block 316, which is directed at the stained, physically, chemicallyor otherwise transformed tissue at block 318. The transmitted,reflected, or otherwise not absorbed electromagnetic radiation isdetected by at least one detection device at block 320. In someembodiments, more than one detection device can be utilized to reduceerror in detection or to collect three-dimensional data.

Based on the detected data, a final image is generated at block 322. Insome embodiments, each pixel or group of pixels in the final image isassociated with a waveform signature. A computing system analyzes thisfinal image and detects the waveform associated with each pixel or groupof pixels at block 324. In other embodiments, the computing system isconfigured to determine the chemical properties of the stained orotherwise altered tissue at a particular point or area. In certainembodiments, the blocks 316 through 324 can be repeated to store finalimages, and the associated waveform signature data, of a stained orotherwise transformed tissue sample under different types ofelectromagnetic radiation sources as depicted by 326.

In another embodiment, the unstained tissue sample is not actuallystained or tagged. Rather, the initial image itself is colored or isotherwise transformed according to a user's choice to generate a finalimage. For example, in embodiments where a virtual staining transform isbeing developed for imaging modalities including but not limited toX-ray, ultrasound, infrared, MRI, PET and/or CT, a user can selectivelycolor or otherwise transform the initial image to a final image that ismore helpful to understand or analyze.

With both the initial image(s) of an unstained sample and the finalimage(s) of an actually stained or otherwise transformed sample stored,the computing system of an embodiment can identify how an input pixel ofthe unstained sample associated with a particular waveform resulted inan output pixel associated with a particular waveform after the stainingor other transformation at block 328. To do so, in some embodiments, thecomputing system identifies and stores the waveform signaturesassociated with each corresponding pixel or group of pixels before andafter the staining or transformation. This process from block 302through block 328 can be repeated in some embodiments for differenttissue samples to reduce error and/or to build a larger database.

In some embodiments, the computing system may detect that an input pixelor group of pixels associated with an identical or substantially similarwaveform signature is inconsistently converted to a pixel or group ofpixels associated with different waveform signatures in subsequenttrials of data collection. In an embodiment, the process from block 302through block 328 or selected block(s) thereof can be repeated until thediscrepancy rate is lowered below a predetermined level. In anotherembodiment, the different conversion results are averaged out at block332 and stored as the output waveform signature corresponding to theinitial pixel or group of pixels associated with a particular waveform.Such conversion data is aggregated and saved in the computing system atblock 334 as a virtual staining transform for a particular stain(s). Inan embodiment, the method described above is repeated for differentstains and/or other transformations or modification of the tissuesample. The individual virtual staining transforms thus developed can becombined to develop a single virtual staining transformation matrixcontaining all or some data related to the virtual transform of multiplestains and/or modifications.

In the embodiment illustrated in FIG. 3B, visible light is directed atthe stained, physically, chemically or otherwise transformed tissue atblock 336. Light that is transmitted, reflected, or otherwise notabsorbed by the stained tissue sample is detected by at least onedetection device at block 338. In some embodiments, more than onedetection device can be utilized to reduce error in detection or tocollect three-dimensional data. Based on the detected data, a finalimage is generated at block 340. In some embodiments, the computingsystem identifies each pixel or group of pixels that comprise thegenerated image according to color or other identifiable characteristicsat block 342.

In another embodiment, the unstained tissue sample is not actuallystained or tagged. Rather, the initial image itself is colored or isotherwise transformed according to a user's choice to generate a finalimage. For example, in embodiments where a virtual staining transform isbeing developed for imaging modalities including but not limited toX-ray, ultrasound, infrared, MRI, PET and/or CT, a user can selectivelycolor or otherwise transform the initial image to a final image that ismore helpful to understand or analyze.

With both the initial image(s) of an unstained sample and the finalimage(s) of an actually stained or otherwise transformed sample stored,the computing system of an embodiment can identify how an input pixel ofthe unstained sample associated with a particular waveform transformedto an output pixel after the staining or other transformation at block344. To do so, in some embodiments, the computing system identifies andstores the waveform signatures associated with each pixel or group ofpixels before the staining or transformation and the correspondingoutput pixel or color thereof after the staining or transformation. Thisprocess from block 302 through block 344 can be repeated in someembodiments for different tissue samples to reduce error and/or to builda larger database.

In some embodiments, the computing system may detect that an input pixelor group of pixels associated with an identical or substantially similarwaveform signature is inconsistently converted to a pixel or group ofpixels with different colors or other identifiable characteristics. Inan embodiment, the process from block 302 through block 344 or selectedblock(s) thereof can be repeated until the discrepancy rate is loweredbelow a predetermined level. In another embodiment, the differentconversion results are averaged out at block 346 and stored as theoutput pixel or group of pixels corresponding to the initial pixel orgroup of pixels associated with particular waveforms. Such conversiondata is aggregated and saved in the computing system at block 348 as avirtual staining transform for a particular stain(s). In an embodiment,the method described above is repeated for different stains and/or othertransformations or modification of the tissue sample. The individualvirtual staining transforms thus developed can be combined to develop asingle virtual staining transformation matrix containing all or somedata related to the virtual transform of multiple stains and/ormodifications.

Preparation for Developing a Virtual Staining Transform for a ParticularStain, Tag, or Both

FIGS. 4A-4F are block diagrams depicting overviews of embodiments ofmethods of collecting data and building virtual staining transforms.FIGS. 4A and 4B illustrate overviews of embodiments of methods ofcollecting data and building virtual staining transforms for particularstains. FIGS. 4C and 4D illustrate overviews of embodiments of methodsof collecting data and building virtual staining transforms forparticular stains when used together with particular tags. FIGS. 4E and4F illustrate overviews of embodiments of methods of collecting data andbuilding virtual staining transforms for particular tags when used alonewithout stains.

The top portions of FIGS. 4A-4F depict analyzing an image of a tissuesample before staining or other alteration. In an embodiment,multi-spectrum electromagnetic radiation 402 is directed at an unstainedtissue sample 404. The unstained tissue sample 404 is not otherwisechemically altered from its natural state. In certain embodiments, someof the multi-spectrum electromagnetic radiation 402 is reflected,transmitted, or otherwise not absorbed 406 by the unstained tissuesample 404. Such multi-spectrum electromagnetic radiation 402 that isreflected, transmitted, or otherwise not absorbed 406 by the unstainedtissue sample 404 is detected by a detector device 104. In certainembodiments, the detector device 104 transmits or otherwise sends thedetected data 408 to an image generating system or device that generatesan image 410 of the detected data. The generated image is made up ofindividual pixels or group of pixels 412 that each has a specificwaveform and/or waveform signature 414 associated with the position ofthe pixel or group of pixels. Each waveform and/or waveform signature414 can be analyzed by determining wavelength amplitudes for specificspectrum wavelengths. In some embodiments, a computer system isconfigured to graph the wavelength amplitude per spectrum wavelength foreach pixel or group of pixels.

Developing a Virtual Staining Transform for a Particular Stain UsedAlone

The bottom portions of FIGS. 4A and 4B depict analyzing images of tissuesamples after staining with a particular stain or dye. In the depictedembodiments, the tissue sample without staining 404 is subsequentlystained with a particular stain or dye. In the embodiment illustrated inFIG. 4A, multi-spectrum electromagnetic radiation 402 is directed at thestained tissue sample 416. In the embodiment illustrated in FIG. 4B,visible light 432 is directed at the stained tissue sample 416. Incertain embodiments, some of the multi-spectrum electromagneticradiation or visible light is reflected, transmitted, or otherwise notabsorbed 418, 434 by the stained tissue sample 416. Such multi-spectrumelectromagnetic radiation or visible light that is reflected,transmitted, or otherwise not absorbed 418, 434 by the stained tissuesample 416 is detected by a detector device 104. In certain embodiments,the detector device 104 transmits or otherwise sends the detected data420,436 to an image generating system or device that generates an image422, 438 of the detected data. The generated image is made up ofindividual pixels or group of pixels 424, 440.

In the embodiment illustrated in FIG. 4A, the computing system furtheranalyzes waveforms 426 associated with each pixel or group of pixels 424in the generated image 422. In certain embodiments, each waveform and/orwaveform signature 426 can be analyzed by determining wavelengthamplitudes for specific spectrum wavelengths. In some embodiments, acomputer system is configured to graph the wavelength amplitude perspectrum wavelength for each waveform associated with each pixel orgroup of pixels. In some embodiments, a computer system analyzes thewaveform and/or waveform signature of both the unstained tissue sample404 and the stained tissue sample 416 at block 428. The detectedwaveforms of the unstained tissue sample 404 and the stained tissuesample 416 are identified and stored by the computer system to determinehow a pixel or group of pixels associated with a specific waveform orwaveform signature changed after the staining to a subsequent pixel orgroup of pixels associated with a waveform or waveform signature. Suchinformation about how each pixel changed in waveform and/or waveformsignature after the staining is stored and combined by the computersystem in some embodiments to develop a virtual staining transform ortransformation function 430.

In the embodiment illustrated in FIG. 4B, the computing system analyzeseach pixel or group of pixels 440 in the generated image 438 accordingto color or some other identifiable characteristic at block 442. In someembodiments, the computer system determines how an input pixel or groupof pixels 412 associated with a specific waveform 414 changed afterstaining to an output pixel or group of pixels 440 of a particular coloror other identifiable characteristic. Such information is stored andcombined by the computer system in some embodiments to develop a virtualstaining transform or transformation function 444. Possible algorithmsfor developing virtual staining transforms comprise but are not limitedto an extended Markov blanket approach, outlier detection based onKullback-Leibler divergence, SVM with multi-classification algorithm,SVM-RFE and Markov blanket for high dimensional fluorescence data, orany other algorithm suitable for such purposes that is either currentlyknown or will be developed in the future.

Developing a Virtual Staining Transform for a Particular Stain and TagUsed in Combination

The bottom portions of FIGS. 4C and 4D depict analyzing images of tissuesamples after staining with a particular stain or dye and attaching aparticular tag. In the depicted embodiments, after analyzing theunstained tissue sample 404, the unstained and untagged tissue sample404 is subsequently stained 416 and tagged 446. Tagging can be optionalin some embodiments. A tag can comprise but is not limited to antibodiesand/or aptamers with or without a label or fluorescent protein. In someembodiments, a tag can be configured to bind to specific receptors inthe tissue sample. In certain embodiments, the presence of the proteinor tag can cause electromagnetic radiation to be absorbed or reflecteddifferently, which can help amplify and/or label the tissue sample forbetter detection and/or identification. For example, use of a tag orprotein can be helpful in situations where different tissue, proteins,pixels or group of pixels of the sample to be imaged are associated withsimilar waveforms or absorption spectra.

In the embodiment illustrated in FIG. 4C, multi-spectrum electromagneticradiation 402 is directed at the stained and tagged tissue sample 416,446. In the embodiment illustrated in FIG. 4D, visible light 432 isdirected at the stained and tagged tissue sample 416, 446. As describedabove, some of the multi-spectrum electromagnetic radiation or visiblelight is reflected, transmitted, or otherwise not absorbed 448, 462 bythe stained and tagged tissue sample 416, 446. Such multi-spectrumelectromagnetic radiation or visible light that is reflected,transmitted, or otherwise not absorbed 448, 462 by the stained andtagged tissue sample 416, 446 is detected by a detector device 104. Incertain embodiments, the detector device 104 transmits or otherwisesends the detected data 450, 464 to an image generating system or devicethat generates an image 452, 466 of the detected data. The generatedimage is made up of individual pixels or group of pixels 454, 468.

In the embodiment illustrated in FIG. 4C, the computing system furtheranalyzes waveforms 456 associated with each pixel or group of pixels 454in the generated image 452. In certain embodiments, each waveform and/orwaveform signature 456 can be analyzed by determining wavelengthamplitudes for specific spectrum wavelengths. In some embodiments, acomputer system is configured to graph the wavelength amplitude perspectrum wavelength for each waveform associated with each pixel orgroup of pixels. In some embodiments, a computer system analyzes thewaveform and/or waveform signature of both the unstained and untaggedtissue sample 404 and the stained and tagged tissue sample 416, 446 atblock 458. The detected waveforms of the unstained and untagged tissuesample 404 and the stained and tagged tissue sample 416, 446 areidentified and stored by the computer system to determine how a pixel orgroup of pixels associated with a specific waveform or waveformsignature changed after the staining and/or tagging to a subsequentpixel or group of pixels associated with a waveform or waveformsignature. Such information about how each pixel changed in waveformand/or waveform signature after the staining is stored and combined bythe computer system in some embodiments to develop a virtual stainingtransform or transformation function 460.

In the embodiment illustrated in FIG. 4D, the computing system analyzeseach pixel or group of pixels 468 in the generated image 466 accordingto color or some other identifiable characteristic at block 470. In someembodiments, the computer system determines how an input pixel or groupof pixels 412 associated with a specific waveform 414 changed afterstaining to an output pixel or group of pixels 468 of a particular coloror other identifiable characteristic. Such information is stored andcombined by the computer system in some embodiments to develop a virtualstaining transform or transformation function 472.

Possible algorithms for developing virtual staining transforms comprisebut are not limited to an extended Markov blanket approach, outlierdetection based on Kullback-Leibler divergence, SVM withmulti-classification algorithm, SVM-RFE and Markov blanket for highdimensional fluorescence data, or any other algorithm suitable for suchpurposes that is either currently known or will be developed in thefuture.

Developing a Virtual Staining Transform for a Particular Tag Used Alone

The bottom portions of FIGS. 4E and 4F depict analyzing images of tissuesamples after attaching a particular tag to the tissue sample. In thedepicted embodiments, after analyzing the unstained tissue sample 404,the unstained and untagged tissue sample 404 is subsequently tagged witha particular tag 446. A tag can comprise but is not limited toantibodies and/or aptamers with or without a label or fluorescentprotein. In some embodiments, a tag can be configured to bind tospecific receptors in the tissue sample. In certain embodiments, thepresence of the protein or tag can cause electromagnetic radiation to beabsorbed or reflected differently, which can help amplify and/or labelthe tissue sample for better detection and/or identification. Forexample, use of a tag or protein can be helpful in situations wheredifferent tissue, proteins, pixels or group of pixels of the sample tobe imaged are associated with similar waveforms or absorption spectra.

In the embodiment illustrated in FIG. 4E, multi-spectrum electromagneticradiation 402 is directed at the tagged tissue sample 446. In theembodiment illustrated in FIG. 4F, visible light 432 is directed at thetagged tissue sample 446. As described above, some of the multi-spectrumelectromagnetic radiation or visible light is reflected, transmitted, orotherwise not absorbed 474, 488 by the tagged tissue sample 446. Suchmulti-spectrum electromagnetic radiation or visible light that isreflected, transmitted, or otherwise not absorbed 474, 488 by the taggedtissue sample 446 is detected by a detector device 104. In certainembodiments, the detector device 104 transmits or otherwise sends thedetected data 476, 490 to an image generating system or device thatgenerates an image 478, 492 of the detected data. The generated image ismade up of individual pixels or group of pixels 480, 494.

In the embodiment illustrated in FIG. 4E, the computing system furtheranalyzes waveforms 482 associated with each pixel or group of pixels inthe generated image. In certain embodiments, each waveform and/orwaveform signature 482 can be analyzed by determining wavelengthamplitudes for specific spectrum wavelengths. In some embodiments, acomputer system is configured to graph the wavelength amplitude perspectrum wavelength for each waveform associated with each pixel orgroup of pixels. In some embodiments, a computer system analyzes thewaveform and/or waveform signature of both the untagged tissue sample404 and the tagged tissue sample 446 at block 484. The detectedwaveforms of the untagged tissue sample 404 and the tagged tissue sample446 are identified and stored by the computer system to determine how apixel or group of pixels associated with a specific waveform or waveformsignature changed after the tagging to a subsequent pixel or group ofpixels associated with a waveform or waveform signature. Suchinformation about how each pixel changed in waveform and/or waveformsignature after the tagging is stored and combined by the computersystem in some embodiments to develop a virtual staining transform ortransformation function 486.

In the embodiment illustrated in FIG. 4F, the computing system analyzeseach pixel or group of pixels 494 in the generated image 492 accordingto color or some other identifiable characteristic at block 496. In someembodiments, the computer system determines how an input pixel or groupof pixels 412 associated with a specific waveform 414 changed afterstaining to an output pixel or group of pixels 494 of a particular coloror other identifiable characteristic. Such information is stored andcombined by the computer system in some embodiments to develop a virtualstaining transform or transformation function 498.

Possible algorithms for developing virtual staining transforms comprisebut are not limited to an extended Markov blanket approach, outlierdetection based on Kullback-Leibler divergence, SVM withmulti-classification algorithm, SVM-RFE and Markov blanket for highdimensional fluorescence data, or any other algorithm suitable for suchpurposes that is either currently known or will be developed in thefuture.

Other Imaging Modalities

In some embodiments, a virtual staining transform can be developed andapplied for other imaging modalities, including but not limited toX-ray, ultrasound, infrared, Mill, PET and/or CT, or any other imagingmodality currently existing or to be developed in the future. In some ofsuch embodiments, information associated with the tissue sample or otherspecimen to be observed is collected using one of such modalities and istransformed or converted according to a unique virtual stainingtransform mapping and is displayed to a user. FIGS. 5A-5E are blockdiagrams depicting overviews of embodiments of methods of virtuallytransforming a tissue sample that is imaged by various imagingtechniques or modalities.

FIG. 5A illustrates an overview of one embodiment of a method ofvirtually transforming a tissue sample imaged under an X-ray spectrum.In an embodiment, X-ray spectrum 502 comprising a wavelength of about0.01 nm to about 10 nm is directed at a tissue sample 404, which absorbssome of the X-ray and reflects, transmits, or otherwise does not absorbothers. Such reflected, transmitted, or otherwise not absorbed X-ray 504is detected by a detector device 104. This detected data 506 istransmitted or sent to an image generating system or device or computingsystem that is configured to generate an initial image 508 using thedetected data. The generated image is made up of individual pixels orgroup of pixels 510. In some embodiments, each pixel or group of pixels510 is associated with a Hounsfield unit measure or number. EachHounsfield unit measure or number corresponds to the CT density.

In certain embodiments, a computing system converts or transforms theHounsfield unit number associated with each pixel or group of pixels toan output unit or output unit waveform. In some embodiments, thecomputing system transforms the inputted Hounsfield unit numberaccording to a pre-stored virtual staining transform 512, which containstransformation algorithms or data for transforming an inputtedHounsfield unit number to one or more output units or output unitwaveforms. The output unit can be, for example, in gray scale or colorin some embodiments. In certain embodiments, the computing systemgenerates an output image 514 comprising pixels or group of pixelsassociated with the output unit or output unit waveforms according tothe virtual staining transform.

FIG. 5B illustrates an overview of one embodiment of a method ofvirtually transforming a tissue sample imaged under an ultrasoundspectrum. In an embodiment, ultrasound spectrum 516 with a frequencyrange of above about 20 kHz is directed at a tissue sample 404, and someof the ultrasound is reflected, transmitted, or is otherwise notabsorbed. Such reflected, transmitted, or otherwise not absorbedultrasound 518 is detected by a detector device 104 in some embodiments.In certain embodiments, this detected data 520 is transmitted or sent toan image generating system or device or computing system that isconfigured to generate an initial image 522 using the detected data. Insome embodiments, the generated image is made up of individual pixels orgroup of pixels 524. In certain embodiments, each pixel or group ofpixels 524 is associated with a Hertz unit.

In certain embodiments, a computing system converts or transforms theHertz unit associated with each pixel or group of pixels to an outputunit or output unit waveform. In some embodiments, the computing systemtransforms the inputted Hertz unit according to a pre-stored virtualstaining transform 526, which contains transformation algorithms or datafor transforming an inputted Hertz unit to one or more output units oroutput unit waveforms. The output unit can be, for example, in grayscale or color in some embodiments. In certain embodiments, thecomputing system generates an output image 514 comprising pixels orgroup of pixels associated with the output unit or output unit waveformsaccording to the virtual staining transform.

FIG. 5C illustrates an overview of one embodiment of a method ofvirtually transforming a tissue sample imaged under an infraredspectrum. In an embodiment, infrared spectrum 530 with a wavelength ofgreater than about 740 nm is directed at a tissue sample 404, whichabsorbs some of the infrared and reflects, transmits, or otherwise doesnot absorb others. Such reflected, transmitted, or otherwise notabsorbed infrared 532 is detected by a detector device 104 in someembodiments. In certain embodiments, this detected data 534 istransmitted or sent to an image generating system or device or computingsystem that is configured to generate an initial image 536 using thedetected data. In some embodiments, the generated image is made up ofindividual pixels or group of pixels 538. In certain embodiments, eachpixel or group of pixels 538 is associated with a waveform signature.Each waveform signature can represent the detected wavelength andcorresponding amplitudes that are detected by the detector at eachposition in the tissue sample.

In certain embodiments, a computing system converts or transforms thewaveform associated with each pixel or group of pixels to an output unitor output unit waveform. In some embodiments, the computing systemtransforms the inputted waveform according to a pre-stored virtualstaining transform 540, which contains transformation algorithms or datafor transforming an inputted waveform to one or more output units oroutput unit waveforms. The output unit can be, for example, in grayscale or color in some embodiments. In certain embodiments, thecomputing system generates an output image 542 comprising pixels orgroup of pixels associated with the output unit or output unit waveformsaccording to the virtual staining transform.

FIG. 5D illustrates an overview of one embodiment of a method ofvirtually transforming a tissue sample imaged under an MRI spectrum. Inan embodiment, Mill spectrum 544 is directed at a tissue sample 404,which absorbs some of the Mill spectrum and reflects, transmits, orotherwise does not absorb others. Such reflected, transmitted, orotherwise not absorbed MM spectrum 546 is detected by a detector device104 in some embodiments. In certain embodiments, this detected data 548is transmitted or sent to an image generating system or device orcomputing system that is configured to generate an initial image 550using the detected data. In some embodiments, the generated image ismade up of individual pixels or group of pixels 552. In certainembodiments, each pixel or group of pixels 552 is associated with aTesla unit.

In certain embodiments, a computing system converts or transforms theTesla unit associated with each pixel or group of pixels to an outputunit or output unit waveform. In some embodiments, the computing systemtransforms the inputted Tesla unit according to a pre-stored virtualstaining transform 554, which contains transformation algorithms or datafor transforming an inputted Tesla unit to one or more output units oroutput unit waveforms. The output unit can be, for example, in grayscale or color scale in some embodiments. In certain embodiments, thecomputing system generates an output image 556 comprising pixels orgroup of pixels associated with the output unit or output unit waveformsaccording to the virtual staining transform.

FIG. 5E illustrates an overview of one embodiment of a method ofvirtually transforming a tissue sample imaged under PET and/or CTspectrum. In an embodiment, PET and/or CT spectrum 558 is directed at atissue sample 404, which absorbs some of the PET and/or CT spectrum andreflects, transmits, or otherwise does not absorb others. Suchreflected, transmitted, or otherwise not absorbed PET and/or CT spectrum560 is detected by a detector device 104 in some embodiments. In certainembodiments, this detected data 562 is transmitted or sent to an imagegenerating system or device or computing system that is configured togenerate an initial image 564 using the detected data. In someembodiments, the generated image is made up of individual pixels orgroup of pixels 566. In certain embodiments, each pixel or group ofpixels 566 is associated with a Hounsfield and/or Counts unit.

In certain embodiments, a computing system converts or transforms themeasured Hounsfield and/or Counts unit associated with of each pixel orgroup of pixels to an output unit or output unit waveform. In someembodiments, the computing system transforms the inputted Hounsfieldand/or Counts unit according to a pre-stored virtual staining transform568, which contains transformation algorithms or data for transformingan inputted Hounsfield and/or Counts unit to one or more an output unitsor output unit waveforms. The output unit can be, for example, in grayscale or color in some embodiments. In certain embodiments, thecomputing system generates an output image 570 using the comprisingpixels or group of pixels associated with the output unit or output unitwaveforms according to the virtual staining transform.

Developing a Virtual Staining Transform for a Particular Stain UnderMultiple Electromagnetic Radiation Sources

FIGS. 6A and 6B illustrate overviews of embodiments of methods ofcollecting data and building virtual staining transforms for aparticular stain viewed under multiple electromagnetic radiationsources. The top portions of FIGS. 6A and 6B depict analyzing an imageof a tissue sample without staining under a plurality of electromagneticradiation sources. In an embodiment, a plurality of electromagneticradiation sources 602 is directed at a plurality of unstained tissuesamples 604. In some embodiments, each of the plurality ofelectromagnetic radiation sources 602 is individually directed at theplurality of unstained tissue samples 604 in turn. After directing afirst electromagnetic radiation source 602 at the unstained tissuesample 604, a detector device 104 detects first electromagneticradiation 602 that is reflected, transmitted, or otherwise not absorbed606 by the unstained tissue samples 604 in a similar manner as describedabove. In certain embodiments, the detector device 104 transmits orotherwise sends the detected data 608 to an image generating system ordevice that generates an initial image 610 of the detected data. In someembodiments, a computer system is configured to analyze the waveformassociated with each pixel 612 in a similar manner as described above.In the depicted embodiment, this process is repeated for the same tissuesample but under different electromagnetic radiation sources 602. Thisprocess can be repeated for different tissue samples as well.

In the depicted embodiments, the plurality of tissue samples withoutstaining 604 are subsequently stained with a particular stain. In theembodiment illustrated in FIG. 6A, the plurality of electromagneticradiation 602 is directed at the stained tissue samples 616. Asdescribed above, in some embodiments, each of the plurality ofelectromagnetic radiation sources 602 is individually directed at theplurality of unstained tissue samples 604 in turn. In the embodimentillustrated in FIG. 6B, visible light 634 is directed at the stainedtissue samples 616. In some embodiments, electromagnetic radiation orvisible light that is reflected, transmitted, or otherwise not absorbed618, 636 by the stained tissue samples 616 is detected by a detectordevice 104. In certain embodiments, the detector device 104 transmits orotherwise sends the detected data 620, 638 to an image generating systemor device that generates an image 622, 638 of the detected data. Thegenerated image is made up of individual pixels or group of pixels 624,640.

In the embodiment illustrated in FIG. 6A, the computing system furtheranalyzes waveforms 626 associated with each pixel or group of pixels 624in the generated image in a similar manner as described above. In someembodiments, these steps can be repeated for the same tissue sample butunder different electromagnetic radiation sources 602. These steps canbe repeated for different tissue samples as well. In some embodiments, acomputer system analyzes the waveforms and/or waveform signaturesassociated with pixels of both the unstained plurality of tissue samples604 and the stained plurality of tissue samples 616 at block 628. Thedetected waveforms associated with pixels of the unstained tissuesamples 604 and the stained tissue samples 616 are identified and storedby the computer system to determine how a pixel or group of pixelsassociated with a specific waveform or waveform signature changed afterthe staining to a subsequent pixel or group of pixels associated with awaveform or waveform signature.

In the embodiment illustrated in FIG. 6B, the computing system analyzeseach pixel or group of pixels 640 in the generated image 638 accordingto color or some other identifiable characteristic. In some embodiments,the computer system determines how an input pixel or group of pixels 612associated with a specific waveform 614 changed after staining to anoutput pixel or group of pixels 640 of a particular color or otheridentifiable characteristic.

In some embodiments, the computer system may detect that inputted pixelsor group of pixels associated with an identical or substantiallyidentical input waveform are inconsistently transformed after stainingto output pixels associated with different waveforms and/or differentcolors or other identifiable characteristics. When there arediscrepancies in the detected data of how a single input waveform istransformed after staining with the same particular stain, the computersystem in some embodiments determines an average output waveform and/oran average output color or other characteristic after the staining atblock 628 or 642. In certain embodiments, the average output waveformand/or average output color or other characteristic associated with eachpixel of the images of stained tissue samples is stored and combined bythe computer system to develop a virtual staining transform ortransformation function 632, 646. In certain embodiments, virtualstaining transforms 632 for a particular stain can be developed in thegeneral manner described above for each of the plurality ofelectromagnetic radiation sources 602 to obtain a more comprehensivevirtual staining transform for that particular stain.

Possible algorithms for developing virtual staining transforms comprisebut are not limited to an extended Markov blanket approach, outlierdetection based on Kullback-Leibler divergence, SVM withmulti-classification algorithm, SVM-RFE and Markov blanket for highdimensional fluorescence data, or any other algorithm suitable for suchpurposes that is either currently known or will be developed in thefuture.

Developing a Virtual Staining Transform for Multiple Stains UnderMultiple Electromagnetic Radiation Sources

FIGS. 7A and 7B illustrate overviews of embodiments of methods ofcollecting data and building virtual staining transforms for multiplestains viewed under multiple electromagnetic radiation sources.

In the depicted embodiments, a plurality of electromagnetic radiationsources 602 are directed at an unstained tissue sample 404. In someembodiments, each of the plurality of electromagnetic radiation sources602 is individually directed at the unstained tissue sample 404 in turn.After directing a first electromagnetic radiation source 602 at theunstained tissue sample 404, a detector device 104 detectselectromagnetic radiation 602 that is reflected, transmitted, orotherwise not absorbed 702 by the unstained tissue sample 404 in asimilar manner as described above. In certain embodiments, the detectordevice 104 transmits or otherwise sends the detected data 704 to animage generating system or device that generates an initial image 706 ofthe detected data. In some embodiments, a computer system is configuredto analyze the waveform associated with each pixel 708 in a similarmanner as described above. In the depicted embodiments, these steps canbe repeated for the same tissue sample under different electromagneticradiation sources 602.

In the depicted embodiments, the unstained tissue sample 404 or asection thereof is subsequently stained with Stain X 710. In theembodiment illustrated in FIG. 7A, the plurality of electromagneticradiation 602 is directed at the tissue sample stained with Stain X 710.In the embodiment illustrated in FIG. 7B, visible light 634 is directedat the tissue sample stained with Stain X 710. Some of theelectromagnetic radiation or visible light is reflected, transmitted, orotherwise not absorbed 712, 734 by the tissue sample stained with StainX 710. In some embodiments, such electromagnetic radiation or visiblelight that is reflected, transmitted, or otherwise not absorbed 712, 734by the tissue sample stained with Stain X 710 is detected by a detectordevice 104. In certain embodiments, the detector device 104 transmits orotherwise sends the detected data 714, 736 to an image generating systemor device that generates an image 716, 738 of the detected data. Thegenerated image is made up of individual pixels or group of pixels 718,740. In the embodiment illustrated in FIG. 7A, each pixel or group ofpixels can be associated with a particular waveform.

In some embodiments, the unstained tissue sample 404 or a sectionthereof is stained with Stain Y 720. In the embodiment illustrated inFIG. 7A, the plurality of electromagnetic radiation 602 is directed atthe tissue sample stained with Stain Y 720. In the embodimentillustrated in FIG. 7B, visible light 634 is directed at the tissuesample stained with Stain Y 720. Some of the electromagnetic radiationor visible light is reflected, transmitted, or otherwise not absorbed722, 742 by the tissue sample stained with Stain Y 720. Suchelectromagnetic radiation or visible light that is reflected,transmitted, or otherwise not absorbed 722, 742 by the tissue samplestained with Stain Y 722 is detected by a detector device 104. Incertain embodiments, the detector device 104 transmits or otherwisesends the detected data 724, 744 to an image generating system or devicethat generates an image 726 of the detected data. The generated image ismade up of individual pixels or group of pixels 728, 748. In theembodiment illustrated in FIG. 7A, each pixel or group of pixels can beassociated with a particular waveform. In certain embodiments, thegeneral method described above can be repeated for any number ofdifferent stains.

In the embodiment illustrated in FIG. 7A, a computer system furtheranalyzes the waveforms and/or waveform signatures associated with eachpixel or group of pixels of the unstained tissue sample 404, the tissuesample stained with Stain X 710, the tissue sample stained with Stain Y720, and any other tissue samples stained with any other stain at block730. The detected waveforms of the tissue samples stained with Stain X710, Stain Y 720, and any other stain along with waveforms of theunstained tissue sample 404 are identified and stored by the computersystem to determine how an unstained pixel or group of pixels associatedwith a specific waveform or waveform signature changed after eachstaining to a subsequent pixel associated with a waveform or waveformsignature. Such information about how each waveform associated with eachpixel changed in waveform and/or waveform signature after staining witheach particular type of stain is stored and combined by the computersystem in some embodiments to develop a virtual staining transform ortransformation function 732.

In the embodiment illustrated in FIG. 7B, the computing system analyzeseach pixel of images of the tissue sample stained with Stain X 710, thetissue sample stained with Stain Y 720, and any other stained tissuesample at block 750 according to color or some other identifiablecharacteristic. In some embodiments, the computer system determines howan input pixel or group of pixels 708 associated with a specificwaveform changed after staining to an output pixel or group of pixels740, 748 of a particular color or other identifiable characteristic.Such information is stored and combined by the computer system in someembodiments to develop a virtual staining transform or transformationfunction 752.

By employing the general methods of the embodiments described inconnection to FIGS. 7A and 7B, comprehensive virtual staining transformsthat comprise transformation data of more than one stain can bedeveloped. Also, in certain embodiments, virtual staining transforms732, 752 for each particular stain can be developed in the generalmanner described above for each of the plurality of electromagneticradiation sources 602 to obtain an even more comprehensive virtualstaining transform.

Possible algorithms for developing virtual staining transforms comprisebut are not limited to an extended Markov blanket approach, outlierdetection based on Kullback-Leibler divergence, SVM withmulti-classification algorithm, SVM-RFE and Markov blanket for highdimensional fluorescence data, or any other algorithm suitable for suchpurposes that is either currently known or will be developed in thefuture.

Developing a Three-Dimensional Virtual Staining Transform for aParticular Stain

FIGS. 8A and 8B illustrate overviews of embodiments of methods ofcollecting data and building a three-dimensional virtual stainingtransform for a particular stain viewed under multi-spectrumelectromagnetic radiation. In some embodiments, a three-dimensionalvirtual staining transform can be developed in the same general manneras described below. In other embodiments, three dimensional virtualstaining transforms for different electromagnetic radiation sources orfor different stains can be developed by combining the methods describedbelow with other embodiments described herein.

In the depicted embodiments, multi-spectrum electromagnetic radiation402 is directed at an unstained tissue sample 404. In some embodiments,multi-spectrum electromagnetic radiation 402 is directed at theunstained tissue sample 404 from a plurality of directions surroundingthe unstained tissue sample 404. In certain embodiments, the unstainedtissue sample 404 is not otherwise chemically altered from its naturalstate. Some of the multi-spectrum electromagnetic radiation 402 isreflected, transmitted, or otherwise not absorbed 802 by the unstainedtissue sample 404. Such electromagnetic radiation that is reflected,transmitted, or otherwise not absorbed 802 by the unstained tissuesample 404 is detected by a detector device 104. In some embodiments,the detector 104 is configured to capture video data of theelectromagnetic radiation that is reflected, transmitted, or otherwisenot absorbed 802 by the unstained tissue sample 404. In otherembodiments, the detector or a plurality of detectors are positioned andconfigured to detect electromagnetic radiation that is reflected,transmitted, or otherwise not absorbed 802 by the unstained tissuesample 404 to allow generation of a three-dimensional image or space. Inyet other embodiments, the detector or a plurality of detectors 104 areconfigured to capture three-dimensional video data of theelectromagnetic radiation that is reflected, transmitted, or otherwisenot absorbed 802 by the unstained tissue sample 404. For example, thedetector can be configured to obtain two-dimensional images of thetissue sample on the xy plane at varying z depths by changing the focaldepth of the detector. In some embodiments, the detector does not changeits zoom but merely changes the focal depth to obtain xy images atdifferent z depths. By changing the z depth, the detector generallygains or loses certain information, which can be processed according toa predetermined algorithm.

The detector device 104 transmits or otherwise sends the detected data804 to an image generating system or device that generates an initialimage or video 806 of the detected data. In certain embodiments, theinitial image or video 806 is three-dimensional. The generated image orvideo is made up of individual pixels or group of pixels 808. Each pixelor group of pixels can be associated with a particular waveform 810.Each waveform signature can represent the detected wavelength andcorresponding amplitudes that are detected by the detector at eachposition in the tissue sample. In some embodiments, a computer system isconfigured to graph the wavelength amplitude per spectrum wavelength foreach waveform associated with each pixel or group of pixels.

In the depicted embodiments, the unstained tissue sample 404 or asection thereof is subsequently stained or tagged or is otherwisetransformed 812. In the embodiment illustrated in FIG. 8A,multi-spectrum electromagnetic radiation 402 is directed at the stainedtissue sample 812. In the embodiment illustrated in FIG. 8B, visiblelight 432 is directed at the stained tissue sample 812. In certainembodiments, some of the multi-spectrum electromagnetic radiation orvisible light is reflected, transmitted, or otherwise not absorbed 814,828 by the stained tissue sample 812. Such multi-spectrumelectromagnetic radiation or visible light that is reflected,transmitted, or otherwise not absorbed 814, 828 by the stained tissuesample 814 is detected by a detector device 104. In some embodiments,the detector 104 is configured to capture video data of theelectromagnetic radiation or visible light that is reflected,transmitted, or otherwise not absorbed 814, 828 by the stained tissuesample 812. In other embodiments, the detector or a plurality ofdetectors are positioned and configured to detect electromagneticradiation or visible light that is reflected, transmitted, or otherwisenot absorbed 814, 828 by the stained tissue sample 812 to allowgeneration of a three-dimensional image or space. In yet otherembodiments, the detector or a plurality of detectors 104 are configuredto capture three-dimensional video data of the electromagnetic radiationor visible light that is reflected, transmitted, or otherwise notabsorbed 814, 828 by the stained tissue sample 812. For example, thedetector can be configured to obtain two-dimensional xy images of thetissue sample at varying z depths by changing the focal depth of thedetector.

The detector device 104 transmits or otherwise sends the detected data816, 830 to an image generating system or device that generates an imageor video 818, 832 of the detected data. In some embodiments, the imageor video 818, 832 is three-dimensional. The generated image or video ismade up of individual pixels or group of pixels 820, 834.

In the embodiment illustrated in FIG. 8A, each pixel or group of pixelscan be associated with a particular waveform 810. Each waveformsignature can represent the detected wavelength and correspondingamplitudes that are detected by the detector at each position in thetissue sample. In some embodiments, a computer system is configured tograph the wavelength amplitude per spectrum wavelength for each waveformassociated with each pixel or group of pixels. Further, a computersystem can be configured to analyze the waveforms and/or waveformsignatures of the unstained tissue sample 404 and the stained tissuesample 812 at block 824. In some embodiments, the detected waveforms ofthe unstained tissue sample 404 and the stained tissue sample 812 areidentified and stored by the computer system to determine how anunstained pixel or group of pixels associated with a specific waveformor waveform signature changed after staining to a subsequent waveform orwaveform signature. Such information about how each pixel changed inwaveform and/or waveform signature after staining with each particulartype of stain is stored and combined by the computer system in someembodiments to develop a virtual staining transform or transformationfunction 826.

In the embodiment illustrated in FIG. 8B, the computing system can beconfigured to further analyze each pixel 834 of images of the stainedtissue sample 812 according to color or some other identifiablecharacteristic. In some embodiments, the computer system determines howan input pixel or group of pixels 808 associated with a specificwaveform changed after staining to an output pixel or group of pixels834 of a particular color or other identifiable characteristic. Suchinformation is stored and combined by the computer system in someembodiments to develop a virtual staining transform or transformationfunction 838.

In embodiments where three-dimensional data is detected by the detectordevice 104, the computer system can be configured to develop a virtualstaining transform 826, 838 that comprises three-dimensionaltransformation data of pixels or groups of pixels after the staining.Similarly, in embodiments where video data is detected by the detectordevice 104, the computer system can be configured to develop a virtualstaining transform 826, 838 that comprises video data or time-sensitivetransformation data of pixels or groups of pixels after the staining.

Possible algorithms for developing virtual staining transforms comprisebut are not limited to an extended Markov blanket approach, outlierdetection based on Kullback-Leibler divergence, SVM withmulti-classification algorithm, SVM-RFE and Markov blanket for highdimensional fluorescence data, or any other algorithm suitable for suchpurposes that is either currently known or will be developed in thefuture.

Developing a Virtual Staining Transform for a Particular Stain withVolume Averaging

FIGS. 9A and 9B illustrate overviews of embodiments of methods ofcollecting data and building a virtual staining transform for aparticular stain using volume averaging. The embodiments depicted inFIGS. 9A and 9B follow the same methods of developing a virtual stainingtransform as described above.

In some instances, the waveform and/or waveform signature 416 associatedwith a pixel or a group of pixels of an unstained tissue sample's imagecan be the product of more than one substances that exist within thepixel area on the tissue sample. In such cases, the detected waveformand/or waveform signature 416 is a combination or a volume average oftwo or more waveform signatures that correspond to each of the multiplesubstances within the pixel area. For example, the detected waveform 416can be a combination of the waveform associated with Substance A 902 andthe waveform associated with Substance B 904. In an embodiment, thecomputer system is configured to identify that a waveform associatedwith a particular pixel or group of pixels comprises one or morewaveforms.

In the embodiment illustrated in FIG. 9A, the computer system is furtherconfigured to identify each of the one or more waveforms that comprise asingle waveform associated with an input pixel and to match each of theone or more waveforms with one or more output waveforms according to avirtual staining transform. In other embodiments, the computer system isfurther configured to utilize volume averaging to determine a singleoutput waveform for the input waveform that comprises one or morewaveforms.

In the embodiment illustrated in FIG. 9B, the computer system is furtherconfigured to identify each of the one or more waveforms that comprise asingle waveform associated with an input pixel and to match each of theone or more waveforms with one or more output pixels with particularcolors and/or other identifiable characteristic according to a virtualstaining transform. In other embodiments, the computer system is furtherconfigured to utilize volume averaging to determine a single outputpixel with a particular color and/or other identifiable characteristicfor the input waveform that comprises one or more waveforms.

Overview of Virtually Staining a Tissue Sample

FIG. 10 illustrates an overview of one embodiment of a method of using avirtual staining transform to virtually stain a tissue sample withdifferent virtual stains, tags, or other transforms and under differentelectromagnetic radiation sources. In some embodiments, the wholeprocess or selected steps of FIG. 10 are repeated for different stainsand/or for different electromagnetic radiation sources.

At block 1002, a particular electromagnetic radiation source isselected, which is subsequently directed at a an unstained tissue sampleat block 1004. Electromagnetic radiation that is transmitted, reflected,or otherwise not absorbed by the sample is received by at least onedetection device at block 1106. In certain embodiments, more than onedetection device can be utilized to reduce error in detection or tocollect three-dimensional data.

Based on the detected data, an initial image is generated and stored asinput at block 1108. A computing system analyzes this initial image atblock 1010. The initial image comprises pixels or group of pixels. Insome embodiments, each pixel or group of pixels is associated with aparticular waveform 810. Each waveform signature can represent thedetected wavelength and corresponding amplitudes that are detected bythe detector at each position in the tissue sample. In certainembodiments, the computing system is configured to determine thechemical properties of each pixel or group of pixels based on thedetected waveform associated with the pixel or group of pixels. Incertain embodiments, the steps of blocks 1002 through 1010 can berepeated to store initial images and waveforms associated with eachpixel or group of pixels of images of unstained tissue samples underdifferent types of electromagnetic radiation sources as depicted by1012.

In the depicted embodiment, a user selects a virtual stain, tag, orother transform to apply to the tissue sample at block 1014. In someembodiments, the computer system applies the appropriate virtual staintransform corresponding to the selected transformation, stain, or tag toeach pixel or group of pixels of the initial image at block 1016. Incertain embodiments, each pixel associated with an identified waveformis transformed to an output pixel according to the virtual staintransform. For example, the system can be configured to match or tosubstantially match a pixel associated with an identified waveform toone or more output pixels associated with particular waveformspre-stored in the database. In other embodiments, each pixel associatedwith an identified waveform is transformed to an output pixel of aparticular color or other identifiable characteristic according to thevirtual stain transform. For example, the system can be configured tomatch or to substantially match a pixel associated with an identifiedwaveform to one or more output pixels with particular colors.

If there is a match or a substantial match, the system can be configuredto identify an output pixel. In some embodiments, the matching comprisescategorizing a detected waveform according to particular compartments,ranges, and/or bands of waveforms that are pre-determined and stored inthe database. For example, if a detected waveform is within a particularcompartment, range, and/or band of pre-stored waveforms, the system canbe configured to match the detected waveform with that pre-storedcompartment, range, and/or band and identify an output pixel associatedwith the compartment, range, and/or band. The system can be furtherconfigured to map the output pixel(s) to the virtually stained image. Incertain embodiments, output pixels are in color or grayscale.

The computer system generates a virtually stained image at block 1018.In certain embodiments, the steps of blocks 1014 through 1018 can berepeated to generate more than one virtually stained image by applyingdifferent stains, tags, or other transforms to the initial image. Theresults can be stored in the computer system at block 1022. In someembodiments, the user can view and/or compare selected images among theinitial image and any generated images at block 1024.

Generation of Multiple Output Pixels Per Input Pixel

In some embodiments, the virtual staining systems and methods describedherein produce one output pixel for each input pixel. In otherembodiments, the virtual staining systems and methods described hereinproduce more than one output pixel for each input pixel. FIG. 11illustrates an overview of some embodiments of methods of virtuallystaining that generate more than one output pixel for each input pixel.

In an embodiment, the virtual staining process comprises determining asingle output pixel for each input pixel according to the appropriatevirtual staining transform. For example, if the virtual stainingtransform of a particular virtual stain, tag, or other transformationtransforms an input pixel A 1104 associated with an input waveform X toan output pixel B 1108 associated with an output waveform Y, eachinstance of input pixel A 1104 in the initial image is mapped as asingle output pixel B 1108 in the virtually stained image 1106. In otherwords, the virtual staining process maps a single input pixel to asingle output pixel according to the pre-stored virtual stainingtransform that corresponds to the selected virtual stain, tag, or othertransformation. In some embodiments, an output pixel associated with aninput pixel 1104 located on a two-dimensional initial image 1102 ismapped to a two-dimensional virtually stained image 1106 at the same xyposition. In other embodiments, an output pixel associated with an inputpixel located on a three-dimensional initial image or space is mapped toa three-dimensional virtually stained image or space at the same xyzposition. In certain embodiments, an output pixel associated with aninput pixel located on a three-dimensional initial image or space ismapped to a two-dimensional virtually stained image at the same xyposition. In other embodiments, an output pixel associated with an inputpixel located on a two-dimensional initial image is mapped to athree-dimensional virtually stained image or space at the same xyposition and an appropriate z position.

In other embodiments, the system is configured to determine and/orgenerate more than one pixel per each input pixel. In some embodiments,one input pixel 1104 can be mapped to four output pixels 1114 togenerate an output image with twice the number of pixels in length andin width. In certain embodiments, one input pixel 1104 can be mapped tonine output pixels 1120 to generate an output image with three times thenumber of pixels in length and in width. In other embodiments, one inputpixel 1104 can be mapped to multiple output pixels 1126 that are stackedor layered on top of each other. The additional pixels 1114, 1120, 1126can be smaller, larger, or of the same size as the single output pixel1108 associated with the input pixel 1104.

In some embodiments, the additional output pixels are obtained byfurther analyzing the waveform associated with the input pixel. Forexample, in some embodiments, an input pixel can be associated with twoor more waveforms pre-stored in the database. A first portion of awaveform associated with an input pixel can correspond to a firstpre-stored waveform, and a second portion of the waveform associatedwith the input pixel can correspond to a second pre-stored waveform.Further, a virtual staining transform can comprise different outputpixels, waveforms, and/or colors for each of these pre-stored waveforms.In embodiments where only one output pixel is generated per each inputpixel, the plurality of output pixels for pre-stored waveforms that areall associated with a single input pixel can be averaged or combinedaccording to some predetermined algorithm to generate a single outputpixel. However, in embodiments where more than one output pixel isgenerated per each input pixel, all or a subgroup of the plurality ofoutput pixels for the pre-stored waveforms that are all associated withthe single input pixel can be mapped. In certain embodiments, theadditional pixels comprise output pixels obtained from directingdifferent electromagnetic radiation, applying different stains, orinformation obtained by imaging under different modalities, such asX-ray, ultrasound, infrared, Mill, PET and/or CT for example. In otherembodiments, the additional pixels comprise output pixels obtained frominterpolating output pixels obtained from a virtual staining transform.

In some embodiments, these additional pixels are mapped in a random orarbitrary order or position. In certain embodiments, the additionalpixels are mapped onto locations on the virtually stained image 1110,1116, 1122 in order of intensity or concentration. In other embodiments,the additional pixels are mapped onto locations on the virtually stainedimage 1110, 1115, 1122 according to some other pre-determined order oralgorithm.

In some embodiments, the additional pixels are displayed natively. Forexample, if the system is configured to generate four output pixels perinput pixel, the system in some embodiments can immediately display avirtually stained image comprising all or a subgroup of the four outputpixels per input pixel. In other embodiments, the additional pixels arenot initially displayed but can be displayed upon receiving furtherinstructions from the user. For example, the system can first display asingle output pixel 1112 per input pixel 1104. However, once a userdouble clicks on a single output pixel 1112, zooms-in on the virtuallystained image, or performs some other pre-determined instruction, thesystem can display all or a subgroup of the four output pixels 1114 perinput pixel. A system configured to generate nine output pixels 1120 perinput pixel 1104 can also either natively display all or a subgroup ofnine pixels 1120. Alternatively, the system can display all or asubgroup of the nine pixels 1120 upon receiving further instructionsfrom a user.

In an embodiment, the additional pixels are not mapped or placed next toeach other but are stacked or layered on top of each other. In someembodiments, the system can be configured to initially display only oneoutput pixel 1124 per input pixel 1104. However, additional outputpixels 1126 corresponding to the same input pixel 1104 are in factdetermined by the system and stored in a “stack” in the background. Uponreceiving further instructions from a user, the system can be configuredto “toggle” between different output pixels 1126 and display thedifferent output pixels 1126. In other embodiments, the system can beconfigured to overlay and display output pixels associated with morethan one imaging modality, stain, tag, or other transform together suchthat a plurality of output pixels can be viewed as a single outputpixel.

If both the initial image or space and the virtually stained image orspace are three-dimensional, the virtually stained image or space can betwice the size or more in length, width, and depth as the initial imageor space. In some embodiments, one input pixel can correspond to eightoutput pixels to generate an output image with twice the number ofpixels in length, width, and depth. In other embodiments, one inputpixel can correspond to 27 output pixels to generate an output imagewith three times the number of pixels in length, width, and depth. Theadditional pixels can comprise any of those described above in relationto two-dimensional images. Further, the manner in which these additionalpixels are displayed can further follow any of those described above.

Multiple Virtual Stains Applied to a Single Tissue Sample

As described above, more than one virtual stain, tag, or other transformcan be applied to a single tissue sample according to some embodiments.FIGS. 12A and 12B illustrate embodiments of methods of virtuallystaining a single tissue sample with multiple virtual stains, tags, orother transforms.

The same general method of analyzing a tissue sample by pixel or groupof pixels under an electromagnetic radiation source as described aboveapplies to the embodiments illustrated in FIGS. 12A and 12B. After eachpixel or group of pixels of the initial image is analyzed and associatedwaveforms and/or waveform signatures 416 are determined, a computersystem in some embodiments applies multiple virtual stains to the inputwaveforms detected from an initial image of the tissue sample to produceoutput images that are virtually stained, tagged, or otherwisetransformed at block 1202. For example, the computer system can generateimages of the tissue sample virtually stained with Stain A 1204, Stain B1210, Stain C 1216, Stain D 1222, and/or any other stain as selected bythe user and available in the database.

Each generated image 1204, 1210, 1216, 1222 corresponding to eachvirtual stain, tag, or other transform comprises pixels or groups ofpixels 1206, 1212, 1218, 1224. In the depicted embodiments, each pixelor group of pixels is associated with a waveform 1208, 1214, 1220, 1226(as illustrated in FIG. 12A) or a particular color or other identifiablecharacteristic. The computer system maps each input pixel associatedwith a particular waveform 416 to an output pixel according to theselected particular stain, tag, or other transform. For example, if auser instructed the computer system to virtually stain the tissue samplewith Stain A, the computer system maps an input pixel of the initialimage 416 associated with a particular waveform to an output pixel 1206according to a virtual transform for Stain A that is stored in thecomputer database. If the user instructed the computer system tovirtually stain the tissue sample with Stain B, the computer system mapsthe same input pixel associated with the particular waveform to anoutput pixel 1212 according to a virtual transform for Stain B that isstored in the computer database. The same process can be applied to anynumber of virtual stains as selected by the user.

Side-By-Side Display of a Single Tissue Sample Virtually Stained withMultiple Stains

In some embodiments, images of a tissue sample virtually stained withmultiple virtual stains can be viewed side-by-side on a display. FIG.12C illustrates one embodiment of a screen view of a single tissuesample virtually stained by multiple virtual stains. It is understoodthat this is not the only embodiment of such screen view and that otherdesigns or configurations are possible.

In the embodiment as illustrated in FIG. 12C, the initial image of theunstained tissue sample 1228 is displayed side-by-side with a generatedimage of the tissue sample virtually stained with Stain A 1232 and agenerated image of the tissue sample virtually stained with Stain B1236. Further, close-up views of a single area within these images 1230,1234, 1238 can be compared side-by-side in some embodiments.

Examples of Virtual Staining

FIG. 13 illustrates an example of virtually staining as conducted by anembodiment. FIG. 13A depicts an example of a digitally or virtuallystained result of a slide using an embodiment disclosed herein. FIG. 13Bis an image of the same slide when actually stained with H&E stain. Asseen from comparing FIGS. 13A and 13B, the virtually or digitallystained result appears substantially similar to the actually stainedslide.

The four images of FIG. 13C each correspond to which pixels of theoriginal image reflect a particular color, for example red, green, blue,and yellow. The four graphs of FIG. 13D represent the actual underlyingdata of each pixel of FIG. 13C. Each of these four graphs represents theintensity of certain wavelengths of each pixel as detected by thedetection device. The data of a single pixel corresponds to a singleline or single waveform in FIG. 13D.

FIG. 14 illustrates another example of a virtually or digitally stainedtissue sample. In the depicted embodiment, each detected pixel from theinitial detection is assigned a color according to the detected waveformassociated with each pixel. Accordingly, the output as illustrated inFIG. 14 is a pseudo-colored version of an unstained slide.

Hyperspectral Imaging and Identifying Species

Current methods of identifying microorganisms generally involve manuallyanalyzing a single plate on which the microorganism specimen is locatedby mass spectroscopy. A person or automated machine generally carriesout such methods by shining infrared light at each plate ofmicroorganisms to determine whether there is growth or not. Further,human interaction is generally required to determine the identity of themicroorganism species in such methods.

However, in an embodiment, the methods of virtual staining andhyperspectral imaging described above can be used to automaticallyidentify the species of a biological sample or microorganism. In manyinstances, a microorganism will have one type of waveform signatureunder hyperspectral imaging. Waveforms of different microorganisms mayhave one or more peaks, where each peak correlates to some protein ofthe microorganism. The identity of such protein, however, is notnecessary to determine the identity of the microorganism, because thewhole waveform itself can be used to identify the microorganism usingthe hyperspectral imaging methods described above.

In an embodiment, the system has a pre-stored database of waveformsignatures associated with different microorganisms. Such database canbe developed using the general methods described above. Such databasecan be used to match the detected waveform(s) of an unknownmicroorganism sample to a known waveform(s) to identify themicroorganism sample. In some situations, the detected waveform(s) of anunknown microorganism is not completely identical to any of thepre-stored waveforms. In some embodiments, the system identifies apre-stored waveform(s) that is most similar to the detected waveform andcalculates a similarity score. For example, the system can report to auser that there is an 80% chance that the unknown sample is E. coli. Inother situations, different portions of a detected waveform(s) of anunknown microorganism can correspond to different pre-stored waveformscorresponding to different microorganisms. In certain embodiments, thesystem can determine what portion of the unknown sample corresponds to afirst microorganism and what portion corresponds to a secondmicroorganism. For example, the system can report to a user that theunknown plate of microorganisms is 30% E. coli and 70% Bacillusatrophaeus.

In an embodiment, a user can instruct the system to identify thosepre-stored waveforms and corresponding microorganisms when thesimilarity is above a certain threshold level. This threshold level canbe, for example, about 50%, about 60%, about 70%, about 80%, about 90%,about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about97%, about 98%, about 99%, about 100%, or any other value.

In an embodiment, the system is configured to compare a detectedwaveform(s) to one or more compartments, ranges, and/or bands ofwaveforms. For example, the system can have pre-stored differentcompartments, ranges, and/or bands of waveforms that are each associatedwith a particular organisms or microorganism. In some embodiments, thecompartments, ranges, and/or bands can be developed from multiple trialsof detecting the waveform(s) of samples of the same or similar organismsor microorganisms and aggregating the detected waveform(s). In certainembodiments, the system can determine whether a detected waveform(s)fits within or substantially fits within one or more of thesecompartments, ranges, and/or waveforms and further identify the organismor microorganism associated with the one or more compartments, ranges,and/or waveforms.

The methods described above of using hyperspectral imaging to identifymicroorganisms or biological species greatly reduces the time and costassociated with performing such functions. For example, by usinghyperspectral imaging to identify microorganisms, the wholeidentification process or a substantial portion thereof can beautomated. Further, such methods can allow for analysis of multiplesamples of microorganisms at once. In addition, once the identity of theunknown microorganism is determined, the system can further determineand report certain characteristics of that microorganism to the user.For example, such characteristics can comprise what the particularmicroorganism responds to or does not respond to. This can furtherreduce the cost and time associated with determining how to treat anunknown sample of microorganism.

FIGS. 15A and 15B illustrate one embodiment of how hyperspectral imagingcan be used to determine the identity of microorganisms.

Hyperspectral Imaging and Virtual Staining for Clinical Diagnostics

The general methods of virtual staining using hyperspectral imaging asdescribed above can also be applied in clinical diagnostics. Becausehyperspectral imaging generally allows for analysis of more data andmore content, better quality control and assurance of a specimen ispossible for biobanking and downstream molecular diagnostics. Inaddition, inter/intra-specimen similarity score analysis is possibleusing hyperspectral imaging for biomarker discovery, validation, anddevelopment.

By employing the hyperspectral imaging methods described above, it ispossible to analyze an image of a tissue sample according to each pixelor group of pixels and determine the particular waveform associated witheach pixel or group of pixels. In an embodiment, multi-spectrumelectromagnetic radiation or other imaging modality radiation isdirected at a tissue sample of a known disease, condition, subtypethereof, or tissue that is susceptible or responsive to a particulartreatment. Other types of radiation can include fluorescence, X-ray,ultrasound, infrared, MRI, PET, and/or CT spectrums. Electromagneticradiation that is transmitted, reflected, or otherwise not absorbed bythe tissue sample is detected by a detection device. The detected datais analyzed according to each pixel or group of pixels by a computersystem to identify particular waveforms associated with each pixel orgroup of pixels. The detected waveforms are stored in the computersystem as data correlating to the tissue sample of the known particulardisease, condition, subtype(s) thereof, tissue that is susceptible orresponsive to a particular treatment, or pathology. These steps can berepeated for a number of tissues of a number of diseases, conditions,subtypes thereof, tissues that are susceptible or responsive to a numberof treatments, or pathology to develop a more comprehensive database.Meanwhile, waveform data of image pixels of healthy correspondingtissues can also be stored in the computer system as reference data.

In an embodiment, multi-spectrum electromagnetic radiation or otherimaging modality radiation is directed at a tissue sample or specimen tobe tested. Other types of radiation can comprise fluorescence, X-ray,ultrasound, infrared, MM, PET, and/or CT spectrums. The electromagneticradiation that is transmitted, reflected, or otherwise not absorbed bythe tissue specimen is detected by a detection device. The detected datais subsequently analyzed according to each pixel or group of pixels by acomputer system to determine the waveform associated with each pixel orgroup of pixels. The computer system compares the detected pixelwaveforms to the pre-stored database of waveforms associated withvarious tissue samples described above for classification, whether it beprimary diagnosis or ancillary diagnosis. For example, if a particularwaveform obtained from the tissue specimen is sufficiently similar to apre-stored waveform of a tissue sample with a particular disease,condition, pathology, or subtypes thereof, then the correspondingportion of the tissue specimen is diagnosed with that particulardisease, condition, pathology, or subtypes thereof. In some embodiments,waveform data obtained from the tissue specimen can be compared tomultiple waveforms stored in a database to determine whether that tissuespecimen is suffering from any number of diseases, conditions,pathology, or subtypes thereof.

In certain embodiments, the computer system is configured to diagnose aparticular pixel waveform as degraded with a particular disease,condition, pathology, or subtypes thereof when the similarity inwaveforms is above a certain percentage. For example, in someembodiments, the computer system will identify a particular waveform ofa tissue specimen with a particular disease or subtype of disease whenthe waveform's similarity to a known waveform of a particular disease orsubtype of disease is at about 90% or above. In other embodiments, thisthreshold value is about 50%, about 60%, about 70%, about 80%, about90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%,about 97%, about 98%, about 99%, about 100%, or any other value.

In an embodiment, the system is configured to compare a detectedwaveform(s) to one or more compartments, ranges, and/or bands ofwaveforms. For example, the system can have pre-stored differentcompartments, ranges, and/or bands of waveforms that are each associatedwith a particular disease, condition, pathology, or subtypes thereof. Insome embodiments, the compartments, ranges, and/or bands can bedeveloped from multiple trials of detecting the waveform(s) of sampleswith the same or similar disease, condition, pathology, or subtypesthereof and aggregating the detected waveform(s). In certainembodiments, the system can determine whether a detected waveform(s)fits within or substantially fits within one or more of thesecompartments, ranges, and/or waveforms and further identify the disease,condition, pathology, or subtype thereof associated with the one or morecompartments, ranges, and/or waveforms.

In some embodiments, the computer system further pseudo-colors eachpixel of the tissue specimen image according to a pre-stored database.For example, pixels associated with waveforms that correspond to thoseof healthy tissue are colored blue, while pixels associated withwaveforms that correspond to those of diseased tissues are colored red.In other embodiments, the transform does not pseudo-color every pixel,but colors those pixels associated with waveforms that correspond todegraded tissues. In yet other embodiments, the output pixels of thevirtual transform are not colored but are in grayscale. The outputpixels are combined by a computer system to generate an output image ofthe tissue specimen that facilitates analysis or diagnosis of the tissuespecimen. In other embodiments, the computer system does notpseudo-color or assign a particular grayscale shade to each pixel, butsimply alerts a user in some manner of pixels associated with waveformsthat correspond to degraded, unhealthy, or otherwise undesirable tissue.

In an embodiment, the system can also be configured to determine andsuggest to a user a particular treatment for the tested tissue sample.For example, in some embodiments, the system can be configured tocompare the detected waveforms from the tissue sample to known waveformsthat correspond to tissue that are susceptible or responsive to certaintreatments. Based on the comparison, the system can suggest a particulartreatment for the tested tissue sample. Such treatments can include, forexample, a particular drug, therapy, chemotherapy, radiation therapy,drug delivery method, among others. The system can be configured tosuggest a particular treatment when the detected waveform issufficiently similar to a pre-stored waveform of a tissue that issusceptible or responsive to a particular treatment. Such thresholdvalue can be, for example, about 50%, about 60%, about 70%, about 80%,about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about96%, about 97%, about 98%, about 99%, about 100%, or any other value.

In an embodiment, the system is configured to compare a detectedwaveform(s) to one or more compartments, ranges, and/or bands ofwaveforms. For example, the system can have pre-stored differentcompartments, ranges, and/or bands of waveforms that are each associatedwith samples that are susceptible or responsive to a particulartreatment(s) or drug(s). In some embodiments, the compartments, ranges,and/or bands can be developed from multiple trials of detecting thewaveform(s) of samples that are susceptible or responsive to the same orsimilar treatment(s) or drug(s) and aggregating the detectedwaveform(s). In certain embodiments, the system can determine whether adetected waveform(s) associated with a sample fits within orsubstantially fits within one or more of these compartments, ranges,and/or waveforms and further identify a particular treatment(s) ordrug(s) that the sample is likely to be susceptible or responsive to. Inan embodiment, the system can be configured to first determine theidentity of a disease or subtype of a disease of the tissue sampleaccording to the methods described above. The system can suggest aparticular treatment based on a pre-stored database of treatments thatare known to be effective to the identified disease or subtype ofdisease. For example, in some embodiments, a radiologist or othermedical professional makes a primary diagnosis of a diseased tissue. Thesystem can be employed to make an ancillary diagnosis or furtherclassify the tissue sample according to a classification system orcategorization within that disease and/or further suggest a particulartreatment that is known to be effective for that category of theidentified disease. In other embodiments, the system can be employed tomake the primary diagnosis as well using the methods described above.

FIG. 16 depicts an example of one embodiment of a method of usinghyperspectral imaging and detected waveforms to further sub-classify atissue(s) with colon cancer. In the depicted embodiment, fluorescencelight is directed at a tissue specimen with colon cancer. The detectedfluorescence light that is transmitted, reflected, or otherwise notabsorbed by the tissue specimen is analyzed according to each pixel orgroup of pixels of an initial image generated from the detectedfluorescence. Each pixel or group of pixels can be associated with aparticular waveform. In other embodiments, any other type of radiation,including multi-spectrum electromagnetic radiation, X-ray, ultrasound,infrared, MRI, PET, and/or CT spectrum can be directed at the sample.These waveforms are compared to a pre-stored database to determinewhether a portion of the tissue specimen corresponding to each pixel isof a particular type of colon cancer.

In the depicted embodiment, waveforms associated with each pixel of aninitial image of a tissue specimen or multiple tissue specimen arecompared to a pre-stored database of waveforms associated with varioustypes of colon cancer for ancillary diagnosis. Colon cancer, or any typeof cancer in general, can be further classified as well-differentiated,moderately differentiated, or poorly differentiated. The system andmethods described herein can provide means to easily classify aparticular tissue or region of a tissue with cancer aswell-differentiated, moderately differentiated, or poorlydifferentiated. In other embodiments, the system and methods describedherein can also be used to make the primary diagnosis whether a patienthas cancer or a particular type of cancer, such as colon cancer, aswell.

In the depicted embodiment, once waveforms of a tissue specimen withcolon cancer are identified, they are compared to a database containingwaveforms associated with well-differentiated, poorly differentiated,and moderately differentiated colon cancers. In other embodiments, acolon cancer specimen or other tissue sample can be sub-classifiedaccording to any other classification or category. For example, thesystem can be configured to classify the tissue sample or regions withinthe tissue sample according to a disease type, a subtype of a disease,whether the region of the tissue will respond to a certain treatment orwhether it is susceptible to a certain treatment, whether the region ofthe tissue is degraded or is healthy, among others. If there is a matchor a sufficiently close match between the detected waveform(s) and thewaveforms in the stored database, the system classifies the waveformaccordingly and reports to a user. The similarity threshold for matchinga detected waveform to a pre-stored waveform can be set at variouslevels. For example, the similarity threshold can be set to about 50%,about 60%, about 70%, about 80%, about 90%, about 91%, about 92%, about93%, about 94%, about 95%, about 96%, about 97%, about 98%, about 99%,about 100%, or any other value.

In the depicted example, a first waveform 1602 corresponds to a storedwaveform associated with well-differentiated colon cancer or a goodtissue. The pixel with this waveform can further be pseudo-colored asblue, or any other color, by the system for convenience of the user. Onthe other hand, a second waveform 1604 corresponds to the waveformassociated with a poorly differentiated colon cancer or degraded tissue.The pixel with this waveform can further be pseudo-colored as red, orany other color, by the system for convenience of the user. If thesystem is configured to pseudo-color each pixel according to itswaveform, as described above, these pixels can be combined by the systemto output a single image that is colored accordingly so that a user caneasily see which parts of the tissue has well differentiated or poorlydifferentiated colon cancer.

In an embodiment, once a tumor specimen is sub-classified using themethods described above, the system can be further configured toidentify an appropriate treatment for that tissue or patient. Forexample, the system can comprise pre-stored data of what type oftreatment(s) each particular type of cancer or subtype of cancer (or anyother disease) responds to. Once the system identifies, classifies, orsub-classifies a tissue specimen according to certain categories, thesystem can suggest a particular treatment(s) to the user depending onsuch categorization. For example, the system can be configured todetermine and report whether the patient or particular tissue of thepatient will respond to chemotherapy, a particular type of chemotherapy,radiation treatment. The system can further be configured to determineand report whether the patient or a particular tissue of the patientwill like be susceptible or resistant to a particular treatment(s), suchas chemotherapy, a certain type of chemotherapy or radiation treatment.

FIG. 17 depicts an example of one embodiment of a method of usinghyperspectral imaging and detected waveforms to further classifymoderately differentiated colonic adenocarcinoma according tohistopathologic classifications. In the depicted embodiment,electromagnetic radiation is directed at multiple tissue samples withmoderately differentiated colonic adenocarcinoma. Given that 90% ofcolon tumors fall into this category, further classification can beuseful. Once the waveforms associated with pixels of initial images ofthese samples are identified by the methods described above, thedetected waveforms can be compared to known, pre-stored waveformsassociated with various kinds of tissues. These pre-stored waveforms cancomprise those associated with tissues with tumor or necrosis, withvarying levels of protein/DNA integrity, those that are acceptable forclinical trials or molecular diagnosis, or those that are responsive orsusceptible to a particular treatment, among others. The system can beconfigured to match the detected waveforms to one or more of theaforementioned pre-stored waveforms when the waveforms are sufficientlysimilar. For example, this threshold level can be set at about 50%,about 60%, about 70%, about 80%, about 90%, about 91%, about 92%, about93%, about 94%, about 95%, about 96%, about 97%, about 98%, about 99%,about 100%, or any other value.

In an embodiment, the system can be further configured to calculate andreport a numerical similarity score of the detected waveform to one ormore pre-stored waveforms. Depending on the type of pre-stored waveformused as a point of comparison, the system can sub-classify orcharacterize the tissue sample according to a number of metrics. Forexample, the system can determine the percentage of tumor or necrosispresent in the tissue sample by comparing the detected waveforms towaveforms associated with tumor or necrosis. In addition, the system candetermine whether the tissue sample is appropriate for clinical trial ormolecular diagnosis by comparing the detected waveforms to waveformsassociated with tissues that are known to be appropriate for aparticular clinical trial or molecular diagnosis. Further, the systemcan determine the protein/DNA integrity of the tissue sample bycomparing its waveforms to those associated with differing protein/DNAintegrity. Using such further characterization or classification of thetissue sample, either the system or a medical professional can determinean appropriate treatment.

FIG. 18 depicts an example of one embodiment of a method of usinghyperspectral imaging and detected waveforms to profile specimens acrossa microenvironment. In the depicted embodiment, waveforms associatedwith different regions of a single tissue sample are compared topre-stored waveforms associated with tissues with in-situ diseases,invasive diseases, and host stromal responses. The system can be furtherconfigured to calculate a numerical similarity score based on each ofthe aforementioned comparisons. Depending on the calculated similarityscore, a medical professional or the system can further determine anappropriate treatment or further testing.

FIG. 19 depicts an example of one embodiment of a method of usinghyperspectral imaging and detected waveforms to further classify andcompare different tumor types. Tissues with certain types of diseasescan have similar associated waveforms with tissues of different types ofdiseases. For example, tissues with certain types of pancreatic cancerscan have similar associated waveforms with tissues with certain types ofcolon cancer. Tissues with these types of pancreatic cancers can beresponsive to the same type of treatment or should be included in thesame clinical trials as tissues with these types of colon cancer. Thesystems and methods described herein can be employed to determine whichtypes of pancreatic cancer are associated with similar waveforms tocolon cancer and thus should be treated with the same means or includedin the same clinical trials.

In the depicted example, waveform signatures associated with a tissuesample with a particular disease or type of cancer are identified. Thedetected waveforms are compared to pre-stored waveforms associated withdifferent tumor types, including benign, premalignant, and stromalresponse. In some embodiments, the system is further configured tocalculate a numerical similarity score based on the comparison. Forexample, the waveform associated with a tissue with type 1 of cancer Acan have a similarity score of 90 when compared to the waveformassociated with a tissue with type 2 of cancer B. If a similarity scoreof 90 is above a set threshold, the system can further be configured toput type 1 of cancer A and type 2 of cancer B in the same category forclinical trials or for a particular treatment. This threshold value canbe set at about 50%, about 60%, about 70%, about 80%, about 90%, about91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%,about 98%, about 99%, about 100%, or any other value.

FIG. 20 depicts an example of one embodiment of a method of usinghyperspectral imaging and detected waveforms to quantitatively grade apathological disease or condition. Existing methods of grading ordiagnosing the severity or procedural state of a particular disease,condition, or subtypes thereof, such as the grading system forhepatitis, are generally subjective. Different medical professionals canhave different opinions as to the severity or procedural state of atissue sample diagnosed with a particular disease, condition, orsubtypes thereof. The methods and systems described herein can provide auniform system of assigning a numerical value to the severity orprocedural state of a particular disease, condition, or subtypesthereof. The disease, condition, or subtype thereof can be anything,including necrosis, fibrosis, inflammation, fat, or iron.

In an embodiment, waveforms associated with tissue samples with varyingstages or severity of a particular disease, condition, or subtypethereof can be identified and stored in a database. These pre-storedwaveforms can be used as milestones for determining the severity orprocedural stage of a tissue with a disease, condition, or subtypethereof to be examined. For example, the waveform associated with ahealthy or normal tissue can be identified and stored. The waveformassociated with a tissue with the worst known case of a particulardisease, condition, or subtype thereof can also be identified and storedas a standard for the worst case. The waveforms associated with anynumber of tissues with the same disease, condition, or subtype thereofbut in different stages or severity can also be identified and storedbased on the need. Such stored waveforms can act as points of comparisonfor the tissue sample to be tested. For example, a waveform associatedthe tissue sample to be tested can be detected and compared to one ormore of such standard pre-stored waveforms. Based on the similarity ofthe detected waveform(s) to the pre-stored standard waveforms, thesystem can be configured to calculate a numerical score for the tissuesample, which can be used to determine the severity or procedural stageof the particular disease, condition, or subtype thereof of the tissuesample. The method described above can be applied to any disease,

In the depicted embodiment, one end of the grading spectrum is awaveform associated with a normal tissue. The other end of the gradingspectrum is a waveform associated with a tissue with the worst knowncase of fibrosis. Waveforms associated with a tissue sample to beexamined are obtained and are compared to these two standard waveforms.In other embodiments, the detected waveforms are compared to additionalstandard waveforms associated with tissues with less severe fibrosis aswell. Based on such comparisons, the system calculates a numerical scoreof the severity of fibrosis in the tissue sample according to apre-determined algorithm. For example, in the depicted embodiment, thetested tissue sample was determined to have a numerical score of 33.567.A medical professional can use this numerical score to objectivelydiagnose the state of the pathological disease, condition, or subtypethereof of the tested tissue sample. In some embodiments, the system ora medical professional can use this numerical score to determine anappropriate treatment or drug for the patient as well. In certainembodiments, the system comprises a database with appropriate treatmentsand drugs according to the severity or procedural stage of a particulardisease, condition, or subtype thereof. In some embodiments, the sametreatment or drug can correspond to a range of numerical scores obtainedby the methods described above. From this database, the system cansuggest a particular drug or treatment to the user.

Vector Signature Analysis

The methods discussed above, including but not limited to developing avirtual staining transform, virtually staining a sample, and usinghyperspectral imaging for clinical diagnostics, generally involvedanalyzing waveform signatures associated with each pixel or group ofpixels. In an embodiment, vector signature analysis can be used, eitherconcurrently or as an alternative to waveform signature analysis, toanalyze a tissue sample or other sample. Vector signature analysis canprovide faster processing and/or a reduced data set while maintainingsimilar accuracy to waveform signature analysis. Vector signature canfurther allow representation of a large amount of data for complexcalculations and can achieve economies of scale in computation. FIG. 21is a block diagram depicting an overview of one embodiment of usingvector signature analysis to analyze each pixel of a detected image of atissue sample.

In the depicted embodiment, an image 2102 obtained from a tissue sampleto be analyzed comprises a plurality of pixels 2104. Each pixel furthercomprises a plurality of variables 2106. Such variables 2106 cancomprise but is not limited to the x position, y position, z position,pixel intensity, wavelength of the pixel, waveform, complex waveform,frequency deconvolution, waveform deconvolution, among others. Anynumber of such variables can be mapped to a vector to create a vectorsignature 2108 for each pixel position or pixel 2104. For example, thevector 2108 of a particular pixel can comprise a first component 2110that corresponds to the pixel's x position, a second component 2112 thatcorresponds to the pixel's y position, a third component 2114 thatcorresponds to the pixel's z position, a fourth component 2116 thatcorresponds to the pixel's intensity level, and a fifth component thatcorresponds to the pixel's wavelength. In other embodiments, a vector2108 associated with each pixel 2104 can comprise a subset of thesevariables 2106 or can comprise additional variables as well.

In an embodiment, vector signatures associated with pixels obtained fromimages of tissue samples to be used as standards 2120 can be determinedand stored in a normative database 2118. For example, when developing avirtual staining transform, vector signatures associated with pixelscomprising images of unstained and stained tissue samples 2120 can bedetermined and stored in the database 2118. Data of vector signaturesassociated with different stain values can be used to determine howpixels obtained from an image of another tissue sample are to betransformed to output pixels after virtually staining the tissue sample.Also, vector signatures associated with pixels obtained from images ofnormal or healthy tissue samples or those with particular diseases,conditions, physiology, pathology, morphology, subtypes thereof, or of aparticular stage or severity can be determined and stored in thedatabase as well. These vector signatures can be used by the system aspoints of comparison to match or substantially match vector signaturesobtained from another tissue sample 2108 to classify, sub-classify, ordiagnose the another tissue sample. The system can also determine alevel of similarity between vector signatures obtained from anothertissue sample 2108 and one or more stored vector signatures 2120. Forexample, the system can identify that a detected vector signature of atissue sample matches or is sufficiently similar to a vector signaturelinked to immature teratomas, lung carcinoma, or any other disease,condition, physiology, pathology, or morphology, among others.

In Vivo Applications of Virtual Staining

As described herein, in some embodiments, virtual staining can beperformed on a target biological tissue sample in vivo. A device havingvirtual staining capability may be used in vivo as part of anexploratory and/or surgical procedure. A virtual staining device can bedelivered into a human or other body to facilitate use of hyperspectralimaging in vivo to provide objective analysis of biological tissuewithout surgical removal and/or isolation of the tissue. In someembodiments, virtual staining can be advantageously applied in vivo tofacilitate real-time delivery of appropriate medical treatment to targetsites within the human body.

Referring to FIG. 22 , in some embodiments, a virtual stainingcapability can be provided to a tissue sample within a human body byintegrating a virtual staining device 2202 into a medical probe 2200which can be inserted into the human body. FIG. 22 shows a portion of anexample medical probe 2200 into which a virtual staining device 2202 canbe integrated. The medical probe 2200 can have a proximal portion 2204and a distal portion 2206. In some embodiments, the virtual stainingdevice 2202 can be integrated into a distal portion 2206 of the medicalprobe 2200, for example to facilitate positioning of the virtualstaining device 2202 at or near a target tissue sample within the humanbody. The virtual staining device 2202 may include an electromagneticradiation emitting source and an electromagnetic radiation detector,and/or any other suitable component, to facilitate directing of anelectromagnetic radiation having a bandwidth within desired ranges at atissue sample to be virtually stained and detecting electromagneticradiation reflected and/or transmitted from the tissue sample. Exposureof examined tissue to an electromagnetic radiation source and/or anelectromagnetic radiation detector may be at a distal surface 2208 ofthe probe 2200. In some embodiments, the probe could be part of anendoscope, laparoscope, or other medical instrument. An electromagneticradiation source and/or detector may be exposed to investigated tissuealong other surfaces of the distal portion 2206, and/or other portionsof the medical probe 2200. Hyperspectral imaging information gathered bythe virtual staining device 2202 may be provided to a computing systemfor analysis, including real-time analysis of the information.

In some embodiments, the probe may be adapted for direct access to atarget site, without the use of a distinct tubular access catheter. Ingeneral, whether used with an access sheath or as a stand alone device,the dimensions of the probe can be optimized by persons of skill in theart in view of the present disclosure to suit any of a wide variety oftarget sites. For example, the probe can be used to obtain hyperspectraland other images and data from large and small arteries and veinsthroughout the cardiovascular system, as well as other lumens, potentialspaces, hollow organs and surgically created pathways. Data collectionmay be accomplished in blood vessels, body lumens or cavities, such asthe lymphatic system, esophagus, trachea, urethra, ureters, fallopiantubes, intestines, colon, biliary ducts, spinal canal and any otherlocations accessible by a flexible or rigid probe. The probe may also beadapted for direct advance through solid tissue, such as soft tissue orthrough bone, for site specific diagnosis and treatment.

In some embodiments, a probe generally comprises an elongate bodyextending between a proximal end and a distal functional end. The lengthof the body depends upon the desired access site and the desiredplacement site for the distal end. For example, lengths in the area offrom about 1 cm to about 20 or 30 cm may be useful in applications thatrequire the catheter to be advanced down a relatively short tubularaccess sheath. Longer lengths may be used as desired, such as on theorder of from about 120 cm to about 140 cm for use in percutaneousaccess at the femoral artery for placement of the distal end in thevicinity of the coronary artery. Intracranial applications may call fora different catheter shaft length depending upon the vascular accesssite, as will be apparent to those of skill in the art.

In some embodiments, at least the proximal section of body may beproduced in accordance with any of a variety of known techniques formanufacturing catheter bodies, depending upon the desired clinicalperformance. For example, the body may be formed by extrusion of any ofa variety of appropriate biocompatible polymeric materials. Knownmaterials for this application include high density polyethylene,polytetrafluoroethylene, nylons, PEEK, PEBAX and a variety of others.Alternatively, at least a proximal portion or all of the length of body16 may comprise a spring coil, solid walled hypodermic needle tubing, orbraided reinforced wall, as is understood in the catheter and guidewirearts. Whether metal or polymeric or a hybrid, the body may be hollow orsolid depending upon the nature of the binding system and other desiredcapabilities.

In one example, the body is provided with an approximately circularcross-sectional configuration. Alternatively, generally rectangular,oval or triangular cross-sectional configurations can also be used, aswell as other noncircular configurations, depending upon the method ofmanufacture, desired surface area, flexibility, access pathway and otherdesign considerations that may be relevant for a particular application.

Dimensions outside of the ranges identified above may also be used,provided that the functional consequences of the dimensions areacceptable for the intended purpose of the catheter. For example, thelower limit of the cross section for any portion of body in a givenapplication will be a function of the number of fluid or otherfunctional lumens, if any, contained in the probe, together with thedesired surface area to be available for the binding partner, as will bediscussed.

Probe body in some embodiments should also have sufficient structuralintegrity (e.g., column strength or “pushability”) to permit the probeto be advanced to a desired target site without buckling or undesirablebending.

The distal end of the probe may be provided with an atraumatic distaltip which may include a guidewire exit port in a guidewire lumenembodiment as is known in the art. A radiopaque marker may be providedon the probe body in the case of relatively long probes to facilitatepositioning of the probe as is known in the art. Suitable marker bandscan be produced from a variety of materials, including platinum, gold,and tungsten/rhenium alloy.

In some embodiments, the probe 2200 can include a plurality of virtualstaining devices 2202. In some embodiments, a virtual staining device2202 can be integrated into a distal portion 2206 and/or one or moreother portions of the medical probe 2200 to facilitate collection ofhyperspectral imaging information from target tissue sample within thehuman body. In some embodiments, the probe 2200 can be delivered to atarget site within one or more other delivery apparatuses to facilitatepositioning of the virtual staining device 2202. For example, the probe2200 may be delivered to a target site through a catheter inserted intoa body lumen, such as an artery or vein, for example. In someembodiments, the medical probe and/or any other delivery device throughwhich the virtual staining device is positioned into the human body canhave a flexible and/or rigid portion, and/or other suitablecharacteristics. The probe 2200 and/or any other delivery device mayhave other characteristics common to delivery devices, including but notlimited to features to allow control and/or manipulation of the virtualstaining device, such as curvable and/or steerable in some embodiments,e.g., via the use of pullwires operably connected to a control.

The virtual staining device 2202 may be inserted into a human bodythrough a natural orifice and/or through an incision, such assurgically, laparoscopically, or percutaneously for example. The virtualstaining device 2202 may be inserted into any number of body cavities,including for example the thoracic cavity, the abdominal cavity and/orthe pelvic cavity, for examination of tissue accessible within thecavities. In some embodiments, the virtual staining device may be usedfor analysis of a digestive tract lining. For example, a virtualstaining device may be introduced orally (e.g., swallowed) forinvestigation of a tissue region along the digestive tract (e.g., tissuelining an esophageal tract, the stomach, and/or the intestines).), andcan be configured in some embodiments similar to a capsule endoscopy,wirelessly communicating hyperspectral information to a computer. Insome embodiments, the virtual staining device can be inserted toinvestigate the respiratory tract, including but not limited to tissuelining a nasal cavity, oral mucosa, or portion of the bronchial tree.The virtual staining device may be inserted into other natural orificesfor analysis of target tissue lining the orifice. In some embodiments,the virtual staining device may be inserted into a biological spacethrough an incision made near a target site or distal from a targetsite. In some embodiments, the device need not even enter the body. Forexample, in some embodiments, a device comprising a hyperspectralimaging apparatus can be in the form of a wand-like or otherconfiguration and waved or otherwise positioned in proximity to theskin, eyes, or other externally accessible anatomical structures inorder to screen for or diagnose medical conditions. For example, awand-like hyperspectral imaging apparatus can be positioned in proximityto the skin to screen for or diagnose in real-time (without necessarilya need for surgical biopsy) a cancerous or pre-cancerous lesion such asmelanoma, squamous cell carcinoma, or basal cell carcinoma, for example.

The virtual staining device 2202 may be part of an exploratory medicalprocedure to investigate a particular region of tissue usinghyperspectral imaging. In vivo application of the virtual stainingtechnique may provide a mode of in vivo tissue visualization. In vivovirtual staining may be used to visualize an extended region and/or atargeted region of tissue. In vivo analysis of biological tissue mayfacilitate a minimally invasive method of identifying suspicious tissue.In some embodiments, in vivo analysis can facilitate identification ofsuspicious tissue before the diseased tissue can be identified throughother means of detection. For example, tissue of interest may beidentified for future monitoring, and/or for examination through anothermethod of analysis. In some embodiments, in vivo hyperspectral analysiscan be used to identify a benign tissue mass, avoiding unnecessarysurgical procedures. The virtual staining device 2202 may be positionedin locations otherwise difficult to access. The virtual staining device2202 can be used to visualize various tissues in the human body,including tissues lining various body cavities, and/or tissue on and/orwithin internal organs (e.g., epithelial cells, including or endothelialcells for example).

In some embodiments, the virtual staining device 2302 can be used inconjunction with another diagnostic imaging modality to facilitateplacement of the virtual staining device. For example, the imagingmodality may be used to provide three-dimensional (3-D) visualization ofsurrounding biological tissue, to facilitate placement of the virtualstaining device to a desired location within the biological space. Imageguided placement of a virtual staining device may facilitate targetedapplication of the virtual staining device. For example, once insertedat and/or near the target region within the biological space, thevirtual staining device may then begin to provide analysis of thetissue. Any number of traditional imaging modalities may beadvantageously used in conjunction with a virtual staining device.Suitable imaging modalities can include, but are not limited to,acoustic microscopy (e.g., ultrasound), radiography (e.g., plain filmX-rays, fluoroscopy, mammography), computed tomography, magneticresonance imaging (MRI), and/or endoscopyPET, and others.

In some embodiments, ultrasound-based technology can be used tofacilitate placement of a virtual staining device into the human body.For example, an ultrasound transducer may be placed on an exterior skinsurface over a region of the body in which virtual staining of tissue isdesired, such that an ultrasound scanner can provide real-timevisualization of surrounding tissue to aide positioning of the virtualstaining device (e.g., the virtual staining device 2202 integrated ontothe probe 2200, as shown in FIG. 22 , and/or any other suitable deliverydevice through which a virtual staining device can be delivered). Insome embodiments, a patient and/or a portion of the patient beingexamined may be positioned in a radiography, and/or magnetic resonanceimaging tool, to provide real-time images of internal tissue to guideinsertion of the virtual staining device. For example, fluoroscopy maybe used to collect real time moving images of the digestive system byintroducing a substance which is opaque to X-ray (e.g., barium sulfate)into the digestive system (e.g., through swallowing by the patient,and/or as an enema). For example, magnetic resonance imaging technologymay be used to provide real-time visualization of tissue withinreproductive organs and/or breast tissue.

In some embodiments, an additional imaging modality may be integratedinto a medical probe to use in conjunction with a virtual stainingdevice. Referring to FIG. 23 , in some embodiments, a virtual stainingdevice 2302 can be integrated into a medical probe 2300 for use inconjunction with one or more other diagnostic imaging modalities 2310for insertion of both the virtual staining device 2302 and an additionalimaging modality 2310 into the human body. The diagnostic imagingmodality 2310 may facilitate placement of the virtual staining device2302. For example, the imaging modality may be used to providethree-dimensional (3-D) visualization of surrounding biological tissue,to facilitate placement of the virtual staining device to a desiredlocation within the biological space, facilitating targeted applicationof the virtual staining device.

Referring to FIG. 23 , one or more additional diagnostic imagingmodalities can be integrated into a medical probe 2300 for insertioninto the human body, for example together with a virtual staining device2302. For example, the probe 2300 may include a proximal portion 2304and a distal portion 2306. The additional diagnostic imaging modalitiescan be exposed to tissue at the distal portion 2306 of the probe 2300(e.g., the additional diagnostic imaging modality being exposed totissue samples at a distal surface 2308 of the probe 2300). The virtualstaining device 2302 and/or the additional diagnostic imaging modalitiesmay be exposed to surrounding tissue along surfaces on other portions ofthe probe 2300. In some embodiments, an additional diagnostic imagingmodality may not be integrated onto a probe 2300 but may be deliveredinto the human body through a delivery catheter, the delivery cathetermay or may not be a catheter through which the virtual staining device2302 is delivered. In some embodiments, the additional diagnosticimaging modality may be delivered through a different delivery deviceand/or through a different incision and/or natural orifice.

In some embodiments, a suitable imaging modality 2310 for delivery intoa body cavity to facilitate placement of a virtual staining device 2302can include providing a light source to the region under inspection toilluminate surrounding tissue. In some embodiments, a fiber optictechnology can be used. For example, an additional imaging modality 2310may include endoscopy, using an endoscope for visualization within abiological space to facilitate positioning of the virtual stainingdevice 2302. Endoscopy may be used to facilitate positioning of thevirtual staining device 2302 in a variety of spaces within the humanbody, including for example within the digestive tract, the respiratorytract, the urinary tract, a reproductive organ, and/or any other organinto which an endoscope may be inserted.

In some embodiments, a virtual staining device can be used together withone or more surgical instruments to provide both in vivo real-timehyperspectral analysis and treatment of biological tissue. The surgicalinstrument and a virtual staining device may be incorporated into acommon medical probe (e.g., 2300) and/or may be delivered through ashared delivery catheter. Various configurations of the medical probeand/or other deliver device may be suitable to facilitate access of thevirtual staining device and the surgical tool to the target tissue. Insome embodiments, the one or more surgical instruments may not beintegrated onto a shared probe and/or be delivered through a shareddelivery catheter. In some embodiments, the one or more surgicalinstruments may be delivered through a different delivery device and/orthrough a different incision and/or natural orifice. In someembodiments, the probe is configured such that the hyperspectral orother diagnostic component is operably connected to a processor that thepatient has a particular medical condition. The processor could thenalert the operator to manually activate the therapeutic component of theprobe, or automatically activate the therapeutic portion of the probe.

For example, a virtual staining device may be inserted into the humanbody together with a surgical tool capable of performing real-timeoperation on a target tissue site and/or delivery of medical therapy tothe target tissue site, facilitating minimally invasive operations. Insome embodiments, a virtual staining device can be inserted into abiological space along with surgical tools to facilitate removal oftissue from target sites (e.g., a mechanical cutter, a needle, and/or avacuum-assisted device). Tissue may be sampled for further analysis(e.g., for a biopsy of the tissue sample) and/or may be removed fordisposal (e.g., excision of diseased tissue). In some embodiments, avirtual staining device may be used to identify tissue for in vivodelivery of medical therapy. For example, a virtual staining device anda cryoprobe be inserted into the human body to identify tissue forapplication of cryosurgery in disposing of diseased tissue, and todeliver cryotherapy to the diseased tissue, (e.g., suitable fortreatment of liver cancer, prostate cancer, lung cancer, oral cancers,cervical disorders, and/or hemorrhoids). In some embodiments, a virtualstaining device can be used in conjunction with a laser source and/or anenergy delivery or other therapeutic agent, such as, for example,microwave, radio-frequency ablation, high-intensity focused ultrasoundsource, laser, infrared, incoherent light, thermal (heat and/or cold,ablative or non-ablative), use of vacuum or suction, and the like. Invivo hyperspectral imaging may be used to facilitate delivery of othersuitable therapies.

The probe may further comprise an optional therapeutic reservoir capableof retaining and releasing one or more therapeutic agents, such as drugcompounds, antibodies, stem cells, or other substances. In someembodiments, the drug could be a chemotherapeutic agent, ananti-inflammatory agent, an antibiotic, an anti-thrombotic agent, acombination of the foregoing, or others.

In some embodiments, a virtual staining device may be applied in vivotogether with both one or more additional imaging modalities, and one ormore surgical instruments for operating on target tissue. For example,an additional imaging modality may facilitate positioning of the virtualstaining device and the surgical instruments may facilitate removal ofand/or treatment of identified suspicious tissue samples. The one ormore surgical instruments and/or additional imaging modalities may ormay not be integrated onto a shared probe and/or are delivered through ashared delivery catheter. The one or more surgical instruments and/oradditional imaging modalities may be delivered through a differentdelivery device and/or through a different incision and/or naturalorifice from that used for inserting the virtual staining device. Thevirtual staining device can be utilized to diagnose and treat a varietyof medical conditions, including but not limited to cancer, hyperplasia,pregnancy (including prenatal diagnosis), infectious disease, autoimmunediseases, or inflammatory diseases.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment. Theheadings used herein are for the convenience of the reader only and arenot meant to limit the scope of the inventions or claims.

Although the embodiments of the inventions have been disclosed in thecontext of a certain preferred embodiments and examples, it will beunderstood by those skilled in the art that the present inventionsextend beyond the specifically disclosed embodiments to otheralternative embodiments and/or uses of the inventions and obviousmodifications and equivalents thereof. In addition, while a number ofvariations of the inventions have been illustrated and described indetail, other modifications, which are within the scope of theinventions, will be readily apparent to those of skill in the art basedupon this disclosure. It is also contemplated that various combinationsor subcombinations of the specific features and aspects of theembodiments may be made and still fall within one or more of theinventions. Accordingly, it should be understood that various featuresand aspects of the disclosed embodiments can be combine with orsubstituted for one another in order to form varying modes of thedisclosed inventions. Thus, it is intended that the scope of the presentinventions herein disclosed should not be limited by the particulardisclosed embodiments described above.

What is claimed is:
 1. An apparatus for providing medical therapy to anin vivo tissue sample, the apparatus comprising: a medical probe havinga virtual staining device configured to provide in vivo virtual stainingof the in vivo tissue sample, the virtual staining device comprising anelectromagnetic radiation source for directing an electromagneticradiation at the tissue sample, and a detector for detectingelectromagnetic radiation reflected or transmitted from the tissuesample, a communication interface configured to provide communicationbetween the electromagnetic radiation source and a computer system, orthe detector and the computer system; an initial image generation moduleof the computer system configured to generate an input image from thedetected electromagnetic radiation from the tissue sample, wherein thefirst image comprises a plurality of pixels; a virtual staining moduleof the computer system configured to assign a virtual stain to the inputimage of the in vivo tissue sample based on an identified waveformassociated with each one of a plurality of pixels forming the inputimage; a diagnosis module of the computer system configured to compareinformation obtained from the virtual staining module regarding thetissue sample with a pre-existing database to determine a diagnosis ofthe tissue sample, and a therapeutic element configured to provide themedical therapy to the tissue sample based at least in part on thevirtual stain.